Paper Title: Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution.






Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
    Jingyao WANG
LIMOS, CNRS UMR 6158 
 LPC, CNRS UMR 6533 

Université Clermont Auvergne 
jingyao.wang@uca.frIssam FALIHLIMOS, CNRS UMR 6158 
Université Clermont Auvergne

issam.falih@uca.frEmmanuel BERGERET LPC, CNRS UMR 6533 
Université Clermont Auvergne 
emmanuel.bergeret@uca.fr

    August 5, 2024
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§ ABSTRACT 
[Code is available at: <https://github.com/jingyaojade/SpSt-GCN>] 
Human Activity Recognition (HAR) is a field of study that focuses on identifying and classifying human activities. Skeleton-based Human Activity Recognition has received much attention in recent years, where  Graph Convolutional Network (GCN) based method is widely used and has achieved remarkable results. However, the representation of skeleton data and the issue of over-smoothing in GCN still need to be studied. 1). Compared to central nodes, edge nodes can only aggregate limited neighbor information, and different edge nodes of the human body are always structurally related. However, the information from edge nodes is crucial for fine-grained activity recognition. 2). The Graph Convolutional Network suffers from a significant over-smoothing issue, causing nodes to become increasingly similar as the number of network layers increases.
Based on these two ideas, we propose a two-stream graph convolution method called Spatial-Structural GCN (SpSt-GCN). Spatial GCN performs information aggregation based on the topological structure of the human body, and structural GCN performs differentiation based on the similarity of edge node sequences. 
The spatial connection is fixed, and the human skeleton naturally maintains this topology regardless of the actions performed by humans. However, the structural connection is dynamic and depends on the type of movement the human body is performing. Based on this idea, we also propose an entirely data-driven structural connection, which greatly increases flexibility.  
We evaluate our method on two large-scale datasets, i.e., NTU RGB+D and NTU RGB+D 120. The proposed method achieves good results while being efficient. 



Human Activity Recognition, Deep learning, Skeleton Data, Graph Convolutional Network.






§ INTRODUCTION



Human activity recognition (HAR) is a research field that spans computer science and electronic engineering. Its goal is to identify and classify human activities using algorithms. Human Activity Recognition (HAR) has received significant attention in recent years and has been applied in various fields such as healthcare, sports, security, smart homes, and wearable devices. The aim is to enhance human well-being, safety, and productivity by offering insights and information for decision-making, monitoring, or feedback purposes<cit.>. The HAR process involves several steps: data collection, data preprocessing, feature extraction, and activity recognition<cit.>. 

Various data modalities are used for different scenarios. They could be divided into two groups: visual modality and non-visual modality. RGB and skeleton data are two common types of visual modalities. RGB data is typically captured with a camera and contains a wealth of color and contour information. This data is widely used in security cameras, intelligent robots, and other applications. Skeleton data is usually extracted from RGB data or collected by depth cameras. Compared to RGB data, 3D skeleton data effectively captures spatial-temporal properties. It contains less data but has higher computational efficiency<cit.>. At the same time, it protects privacy. Skeleton data is a good choice when computing resources are limited or privacy protection is required. Acceleration data is typically non-visual data. The acceleration sensor is small, lightweight, and designed to protect privacy. It can accurately and comprehensively measure the spatial acceleration of an object without prior knowledge of its direction of motion. This sensor is commonly used in low-cost and low-power-consumption portable devices, such as smartwatches. Each of the various data modalities has its suitable application scenario. In this article, we focused on visual data, specifically on skeleton data. 

Currently, the predominant approach involves using spatial-temporal convolutional networks to extract features from skeleton data. Researchers utilize GCN to extract spatial features within frames, and then utilize TCN to extract temporal features between frames. The researcher designs the skeletal connection diagram based on the human skeleton's topology. When performing graph convolution, the adjacency matrix is utilized to depict the connections between nodes and perform message passing between neighboring joints<cit.>. The issue is that human beings have symmetrical structures, and there should be not only spatial connections but also structural connections between joints. 

The action of "clapping hands" requires both hands to come together. There is a strong connection between the two hands. However, a mere spatial connection would not adequately represent the connection between the hands, and we need to depict this connection with a structural link. We believe that distinguishing fine-grained activities requires a focus on the activities of edge nodes. Taking typing on the keyboard and writing as an example<cit.>, the most critical metric to distinguish between these two actions is the fine-grained movement of the hand. In terms of spatial structure, edge nodes are the least connected to other nodes and farthest away from the center node. This hinders their ability to effectively aggregate information from other nodes. To address this issue, we propose adding extra structural connections to the edge nodes.


The spatial connection is fixed, and the human skeleton naturally maintains this topology regardless of the actions performed by humans. However, the structural connection should be dynamic and depends on the type of movement the human body is performing. There is a strong connection between the hands when clapping, but not when checking the time (from watch) [19]. Based on this idea, we propose a data-driven method to initialize the structural connections based on the similarity of edge node sequences for various samples, so that each sample has its unique structural connection. 
GCN aggregates node information through edge connections to generate new node representations, which is effective but can lead to the problem of over-smoothing. Different from the normal GCN way of aggregating information, we propose performing node information differentiation through the structural branch. Our differentiation method can enhance the contrast between the edge nodes, and also reduce the issue of over-smoothing to some extent.


The main contributions of this paper are summarized as follows:

    
  * We propose a spatial-structural graph convolution method which can better represent symmetrical human structures and edge nodes.
    
  * Instead of initializing the adjacency matrix solely based on the count of adjacent nodes, we adopt a differential approach to initialize the structural connections of various samples. This differential initialization is contingent on the similarity of edge node sequences, enhancing the flexibility of graph convolution. 
    
  * To address the over-smoothing problem inherent in GCN, we utilize the inverse adjacency matrix to discern information pertaining to edge nodes.


The remainder of this paper is organized as follows: Section 2 presents the state of the art papers related to our work. Our proposed method is highlighted in section 3. Section 4 presents the experimental part including data set description, experimental settings, evaluation metrics, experimental results and their analysis and the conclusion is given in section 5.



§ RELATED WORKS


HAR is a classic temporal classification problem. In earlier years, traditional CNN and RNN models were dominant.
Zhang et al.<cit.> designed a two-stream CNN-RNN model to overcome view dependency. The main idea of this paper is to use view adaptation modeling to obtain a variable observation coordinate, allowing for the generation of skeleton data from different viewpoints. An RNN-based view adaptation subnetwork is integrated into the VA-RNN stream, while a CNN-based view adaptation subnetwork is integrated into the VA-CNN stream. Then, an LSTM classification network is applied in the VA-RNN stream, while a CNN classification network is applied in the VA-CNN stream. 
Caetano et al.<cit.> encoded the magnitude and orientation values of the skeleton joints into the image, and then utilized a CNN network for classification. 


In recent years, several Transformer-based and GCN-based models have appeared.
Plizzari et al. <cit.> developed a two-stream spatial-temporal transformer network. On the S-TR stream, a Spatial Self-Attention module (SSA) is used to analyze intra-frame interactions between different body parts, followed by a 2D convolution on the time dimension (TCN). On the T-TR stream, a Temporal Self-Attention module (TSA) is used to model inter-frame correlations, while spatial features are extracted by a standard graph convolution (GCN). 

Skeleton data used for human activity recognition (HAR) tasks are sequential data that contain consecutive frames. Skeleton data represents natural topography. In addition to being used as a matrix, skeleton data could also be utilized as a graph by a graph neural network <cit.>. In addition, the skeleton data used for action recognition is sequential data. GCN and TCN blocks are often used to capture spatial-temporal features, where Graph Convolutional Network (GCN) is used to capture intraframe spatial information by aggregating information from neighboring nodes, and Temporal Convolutional Network (TCN) is used to capture interframe temporal information.
Yan et al.<cit.> propose the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model, which applies alternating convolution of GCN and TCN, and has become the baseline in the field of skeleton-based action recognition. 
Song et al. <cit.> developed an efficient graph convolutional network. They extended the convolutional layer in CNN to the GCN network to extract temporal dynamics and compress the model size. They then employed a compound scaling method that adjusts the width (number of channels) and depth (number of layers) factors to make the model flexible and effective. In addition, Spatial Temporal Joint Attention (ST-JointAtt) is incorporated into each block, and three input branches are fused early in the network. The above strategies enable the network to achieve good results at a low computational cost. 





 §.§ Skeleton Data Representation


Skeleton data consists of a set of joint coordinates, usually represented as 3D points or 2D projections, along with additional information like joint velocities or orientations. These joint coordinates are often normalized to a consistent scale or relative to a specific reference point to enhance generalizability across individuals and improve recognition accuracy. Skeleton data removes video backgrounds, people textures and outlines, which reduces lots of calculations. 3D skeleton data has a good view-invariance. It is possible to change the data view by rotating the skeleton in 3D space. 

Some researchers use matrices or images to represent skeleton data. Yang et al. <cit.> proposed a Tree Structure Skeleton Image (TSSI) which encodes skeleton data by depth-first tree traversal order to image. Banerjee et al. <cit.> extracted four features from skeleton data (Distance feature, Distance velocity feature, Angle feature, Angle velocity feature). These features are then encoded into single-channel grayscale images and are fed to the CNN. 


Skeleton data is a natural topography. In addition to being used as a matrix, skeleton data could also be represented as a graph. Some researchers represent skeleton data as an undirected graph, which effectively illustrates the connections between joints. The joint information will be aggregated bi-directionally. Yan et al.<cit.> represented the skeleton data using natural bone topology based on prior experience. Instead of using a fixed graph, Shi et al. <cit.> propose a data-driven method to enable the model to adapt to various data samples. According to the pattern of human movement, the central joints always drive the peripheral joints. Shi et al. <cit.> represented the skeleton as a directed acyclic graph to better incorporate joint and bone data. 

In addition to representations based on the original topology, some researchers utilize the Node2vec method to obtain graph embeddings. Laines et al. <cit.> conducted a Depth-First-Search (DFS) on the skeleton tree to obtain a new spatial embedding.  Sun et al. <cit.> utilized the breadth-first search (BFS) algorithm, which commences from the root joint (hip center joint) to rearrange the joints.

In addition to utilizing fixed topological structures, some researchers have employed dynamic structures to represent the skeleton data. Ye et al.<cit.> proposed a Context-encoding Network (CeN) to automatically learn the skeleton topology, incorporating contextual features from the remaining joints in a global manner when learning the dependency between two joints. Cheng et al.<cit.> proposed a non-local shift graph operation, which enables each node to have a receptive field covering the entire skeleton graph, allowing for adaptive learning of the relationships between joints. Chen et al.<cit.> proposed Channel-wise Topology Refinement Graph Convolution (CTR-GC), which learns a shared topology across all channels and refines it with channel-specific correlations for each channel.





§ PROPOSED APPROACH

In this work, we utilize the GCN-TCN block to extract spatial-temporal features, where the GCN layer and TCN layer are alternately employed to capture spatial and temporal dependencies. Temporal convolutions are essentially 2D convolutions with a kernel size of 1, designed to capture the temporal dependence of each node. In this section, we introduce our proposed spatial-structural graph convolution and the generation of the structural adjacency matrix. 



 §.§ Spatial-Structural Graph Convolution (SpSt-GCN)

For GCN-based methods,  the input skeleton data is represented as (V, E) with N joints and T frames. Here, V represents joint points, and E represents connections between nodes, often represented by an adjacency matrix. 

The conventional spatial graph convolution is shown below, where f_in and f_outSpatial represent the input and output features, respectively. A_j represents the adjacency matrix at distances j, Λ_j is a weight vector used to normalize A_j. The parameters W_j can be optimized during the training process. 

    f_outSpatial = ∑_jW_jf_inΛ_j^-1/2A_jΛ_j^-1/2

To increase the flexibility of graph convolution, we add a parameterized matrix B as described in <cit.>. The matrix B is initialized to an all-zero matrix of the same size as adjacency matrix A and optimized together with the other parameters in the training process.

    f_outSpatial = ∑_jW_jf_in(Λ_j^-1/2A_jΛ_j^-1/2+B_j)



Many researchers commonly employ the skeletal topology structure for establishing connections among joints, a methodology known for its efficiency albeit lacking in precision. It is crucial to recognize that the human skeleton, characterized by symmetry, presents a nuanced structure wherein even widely spaced symmetrical joints exhibit robust structural correlations. While prevailing graph convolution techniques generally enable non-edge nodes to efficiently aggregate information from both nearby and distant nodes, limitations arise when considering edge nodes. These edge nodes possess only a singular connection and, as a result, can solely aggregate information from nodes in close proximity to the central point. This asymmetry in information flow becomes particularly evident when comparing edge nodes to central nodes, as the former receives comparatively less neighbor information, thereby impeding the formation of optimal node representations. To address this challenge and enhance the representation of both symmetric and edge nodes, we propose a novel approach named spatial-structural graph convolution. 

This innovative method involves the integration of two branches: a spatial branch responsible for aggregating information from spatially adjacent nodes and a structural branch focused on consolidating information between edge nodes. By combining these branches, the spatial-structural graph convolution aims to achieve a more comprehensive and accurate representation of the underlying skeletal structure, thereby advancing the state-of-the-art in joint connectivity modeling <ref>.
The structural graph convolution is formulated as follows:

    f_outStructural = ∑_jM_jf_inAs_j

where f_in and f_outStructural represent the input and output features, respectively. As_j represents the structural adjacency matrix for distance j. The parameters M_j can be optimized during the training process. In this work, we set the distance as 1.  Then, we use element-wise addition to combine the outputs of these two branches and obtain the final output. 

    f_out = f_outSpatial+f_outStructural






 §.§ Structural Adjacency Matrix


In contrast to the conventional approach of spatial graph convolution, where all samples share a uniform adjacency matrix, we recognize that the structural connection between edge nodes is intricately linked with the specific actions performed. To capture these nuanced relationships, we propose a data-driven structural adjacency matrix, wherein the strength of connections between edge nodes is determined solely by the characteristics of each sample.

In the context of the NTU60 dataset's skeleton data, we observe five edge nodes, each corresponding to a distinct time series. In standard graph convolutional networks (GCN), the adjacency matrix is normalized based on the number of adjacent nodes. However, for structural connections, creating a conventional adjacency matrix between edge nodes is impractical as edge joints lack tangible spatial connections. Aggregating them without differentiation would compromise the unique characteristics of each edge node. Based on this idea, we use the similarity of edge node sequences instead of the number of adjacent nodes to represent the strength of the connection.

Euclidean distance and cosine similarity are widely employed distance calculation methods in deep learning. Euclidean distance quantifies the straight-line separation between two points in Euclidean space, making it well-suited for sequences with consistent lengths and where element order is crucial. Cosine similarity gauges the cosine of the angle between two vectors, treating sequences as vectors in a high-dimensional space. Cosine similarity is suitable for situations involving text data, document comparison, and situations where vector magnitudes are not critical.

In the context of skeleton data, the interplay of human muscles and bones results in a strong correlation among joint movements. However, the execution of force progresses incrementally. Consider playing badminton as an example: the upper arm propels the forearm, the forearm propels the wrist, leading to the final stroke of the shuttlecock. The transmission of the movement unfolds from the core to the periphery, with movement speed and amplitude gradually accelerating. Despite the collective coordination of the entire arm for a given motion, the time and range of movement for different joint points vary. In such cases, neither Euclidean distance nor cosine similarity proves suitable.

Taking inspiration from similarity calculation methods in natural language processing, we turn to the utilization of Dynamic Time Warping (DTW) to compute distances between each pair of edge nodes. DTW is a technique adept at measuring the similarity between two sequences that may exhibit variations in time or speed. It excels in scenarios where sequences share similar patterns but may be temporally misaligned. The DTW algorithm constructs an optimal warping path between the sequences, assessing similarity between corresponding points and allowing for time axis stretching or compressing as needed. The primary objective is to minimize the overall cost of warping while aligning similar features. Notably, DTW accommodates discrepancies in movement time among different joint points and considers the magnitude of movement, making it highly suitable for assessing movement similarity between edge nodes.

We finally use FastDTW for efficiency. We calculate a distance matrix D for each sample. To discern the correlation between different edge nodes, we leverage the inverse of the distance matrix D^-1 as a representation of the correlation matrix. Larger values in D^-1 signify greater similarity in actions between the two edge nodes, indicating a stronger correlation.

Addressing the over-smoothing issue inherent in GCN, where data from different joints tends to become overly similar with each aggregation step, structural GCN takes a different approach. When employing the D-1 approach directly, heightened similarity among adjacent nodes leads to an aggregation of information, thereby exacerbating the likeness of edge nodes, a condition contrary to the desired outcome. Rather than engaging in information aggregation from neighboring nodes, this method prioritizes the preservation of node-specific information by delineating each edge node from others through the utilization of -D-1. Consequently, the application of -D-1 fosters greater differentiation among edge nodes, thereby facilitating the capture of nuanced information.
To achieve this, an identity matrix (I) is introduced to retain the distinct characteristics of each node. The dynamic structural adjacency matrix, denoted as I-D^-1, is then calculated for each sample, providing a nuanced representation of the structural connections within the data.


For a visual representation, we refer to the algorithm in <ref>, illustrating the steps involved in the dynamic structural adjacency matrix calculation. Additionally, Figure <ref> provides visualizations of the spatial-structural connections for three distinct samples—throw, writing, and stand up—underscoring the effectiveness of our proposed approach in capturing the intricacies of fine-grained activity recognition. Figure <ref> provides visualizations of matrix D^-1. Figure <ref> provides visualizations of learned spatial and structural adjacency matrix.














 §.§ Data Preprocessing

In this work, we adopt the same data preprocessing as described in <cit.>. The shape of each action sequence x is (C_in,T_in,V_in), where C_in denotes coordinates , T_in denotes frames, and V_in denotes joints. We extract three input features from the input data: joint features, velocity features, and bone features. 

For joint features, we first calculate the relative joint positions j_r of all joints i (i∈{1,2,...,V_in}) to the center joint c, then concatenate the original joint positions with the relative joint positions. For the NTU RGB+D Dataset and NTU RGB+D 120 Dataset, we selected joint 2 (middle of the spine) as the central joint. Then we concatenate the original joint positions x  with the relative joint positions j_r as joint input features.

    j_r = x[:,:,i]-x[:,:,c]


    j = x ⊕ jr

For velocity features, we calculate slow motion v_s for time t (t∈{1,2,...,V_in}) using one frame as the time interval, and fast motion v_f for time t (t∈{1,2,...,V_in})  using a two-frame time interval.  Then we concatenate the slow motion v_s  with the fast motion v_f as velocity input features.

    v_s = x[:,t+1,:]-x[:,t,:]


    v_f = x[:,t+2,:]-x[:,t,:]


    v = v_s ⊕ v_f

For bone features, we first calculate the lengths of the bones b_l, which consist by joint i (i∈{1,2,...,V_in}) and its adjacent joint i_adj (i_adj∈{1,2,...,V_in}) . Then we calculate bones' angles b_a,w for three coordinates w (w∈{x,y,z}). We concatenate the lengths of the bones b_l with the bone angles b_a,w as bone input features.

    b_l = x[:,:,i]-x[:,:,i_adj]


    b_a,w = arccos(b_l,w/√(b_l,x^2+b_l,y^2+b_l,z^2))


    b = b_l ⊕ b_a





 §.§ Model


For the overall efficiency of the network structure, we utilize the GCN block introduced by <cit.> as the fundamental unit <ref>. However, instead of the conventional GCN layer, we have integrated our novel spatial-structural graph convolution layer. The network architecture begins with an initial block designed to extract features at the outset, followed by the stacking of four GCN blocks to progressively extract higher-dimensional features. Finally, a Classifier block, which incorporates a Global Average Pooling (GAP) layer, a Dropout layer, and a Fully Connected (FC) layer, is employed to generate the output for each modality, as illustrated in <ref>. We then combine the results from three inputs (joint features, velocity features, bone features) to get the final output, where leveraging multiple modalities to enhance the model's capacity for nuanced feature extraction and robust predictive performance.








§ EVALUATION




 §.§ Dataset




  §.§.§ NTU RGB+D Dataset

"NTU RGB+D" dataset<cit.> contains 60 action classes and 56,880 video samples captured from 40 different people using the Microsoft Kinect v2. The 60 action classes can be categorized into three main groups: 40 daily actions, 9 health-related actions, and 11 mutual actions. As mentioned, the NTU RGB+D dataset is a multi-modal dataset that contains RGB videos, depth map sequences, 3D skeletal data, and infrared (IR) videos for each sample. In this work, we only utilize skeleton data. Each frame contains two skeletons, and each skeleton contains 25 joints. 

We evaluated our method using two benchmarks proposed by the authors of this dataset: cross-subject validation (X-sub) and cross-view validation (X-view). In cross-subject validation, we divided the subjects into two groups: twenty subjects in the training data group and another twenty subjects in the validation group. In cross-view validation, the data collected by cameras 2 and 3 are recognized as training data, while the data collected by camera 1 is used for validation. 



  §.§.§ NTU RGB+D 120 Dataset

The "NTU RGB+D 120" dataset<cit.> is an extension of the "NTU RGB+D" dataset, captured from 106 different individuals, with an additional 60 classes and 57,600 video samples. The 120 action classes can be categorized into three main groups: 82 daily actions, 12 health-related actions, and 26 mutual actions. We evaluate our method using two benchmarks proposed by the authors of this dataset: cross-subject validation (X-sub) and cross-setup (X-set120). In cross-subject validation, 53 subjects are divided into a training data group and another 53 subjects into a validation group. In cross-validation, 16 setups are used for training, and the remaining 16 setups are used for testing. 




 §.§ Experimental Setting

In our experiments, we set the training epoch to 50. We apply the same settings as described in<cit.>. Our models are trained with gradient descent (SGD) with a Nesterov momentum of 0.9 and a weight decay of 0.0001. The learning rate is set to 0.1 and decays with a cosine schedule after the 10th epoch. The probability in the dropout layer is set to 0.25. We perform a data transformation as described in <cit.> for the X-view benchmark. The batch size is set to 16 for both the NTU RGB+D dataset and the NTU RGB+D 120 dataset. All our experiments are performed on one NVIDIA GeForce RTX 3090 GPU.




 §.§ Comparisons

 



  §.§.§ Effect of Structural Graph Convolution

 In this part, we will discuss the impact of three input modalities and the impact of our structural branch on X-sub benchmark <ref> and on X-view benchmark <ref>. We can see that all three modalities are essential, and that fusing the scores of the three modalities outputs in the highest accuracy for both X-sub and X-view benchmarks. In addition, using the same network structure, there is a significant improvement in accuracy when employing the spatial-structural graph convolution layer instead of the spatial graph convolution layer.







  §.§.§ NTU RGB+D Dataset

We compare our models with the state-of-the-art methods on the NTU RGB+D dataset in <ref>. We classify the commonly used methods for skeleton-based human activity recognition into two categories: CNN-RNN-LSTM-based methods and GCN-based methods. Notably, the GCN-based method exhibits superior performance compared to CNN-RNN-LSTM-based methods. In recent years, there has been a growing preference among researchers for GCN-based approaches in skeleton recognition tasks, showcasing better overall performance than their CNN-RNN-LSTM counterparts. 
ST-GCN is one of the most well-known GCN-based models for skeleton recognition. Our method greatly exceeds it by 10.1% on the X-sub benchmark and 7.5% on the X-view benchmark. EfficientGCN is another popular model for its high efficiency and high accuracy. In this work, we use the same GCN block and the same number of blocks as EfficientGCN-B0. The accuracy of our model exceeds it by 1.4% on the X-sub bencnmark and 0.9% on the X-view benchmark. CTR-GCN learns topologies dynamically, which demonstrates commendable outcomes. However, it overlooks critical considerations such as the symmetry inherent in human body structure and the constraints associated with edge nodes. We posit that integrating the adaptable topology of CTR-GCN with our refined graph representation will likely yield superior results. We consider that the results of StSp-GCN exceed those of most models due to our precise expression of the skeleton data and the flexibility of graph convolution by introducing unique structural connections for each sample.






  §.§.§ NTU RGB+D 120 Dataset


Compared with the NTU RGB+D data set, the NTU RGB+D 120 dataset is larger and has more classes. This means that there are more similar actions that need to be distinguished, which poses a greater challenge to the model. The overall performance of the GCN-based model is still better than the CNN-RNN-LSTM-based method. Compared to ST-GCN, our method greatly exceeds it by 17.1% on the X-sub benchmark and 15.6% on the X-view benchmark. Our method outperforms EfficientGCN-B0 by 1.2% on the X-sub benchmark and 3.8% on the X-view benchmark. In comparison to CTR-GCN, our introduced structural connections augment the delineation of topology across diverse samples. It is our conjecture that amalgamating our structural links with the inherently flexible topology of CTR will yield more favorable outcomes. Similar to the results on the NTU RGB+D dataset, the performance of SpSt-GCN is mainly attributed to the precise expression of the skeleton data and the flexibility of graph convolution.





  §.§.§ Model Complexity


Considering the importance of efficient resource utilization, we not only assess model accuracy but also consider computational complexity. We compare the model complexity (FLOPs and the number of parameters) with state of art methods on the X-sub benchmark in <ref>. Compared with most models, our model has a higher accuracy while having low computational complexity and parameter quantity. Specifically, our model's FLOPS and parameter count are only 30.02% and 15.48% of ST-GCN, respectively. The computational complexity and parameters of our method have increased relative to EffciientGCN-B0, mainly due to adjustments in model parameters. Compared with Sp-GCN, the FLOPs the number of parameters of SpSt-GCN have only increased by 15.3% and 9.1% respectively, which indicates that the structural branch is very efficient.








§ CONCLUSION

In this article, we propose a Spatial-Structural Graph Convolutional Network (SpSt-GCN) for skeleton-based action recognition to better represent symmetrical human structures and edge nodes and address the issue of over-smoothing in Graph Convolutional Networks. The spatial branch aggregates information based on the topological structure, while the data-driven structural branch performs differentiation based on the similarity of edge node sequences. Our approach yields favorable results while maintaining efficiency. In the future, we will explore how to improve the flexibility of structural connection and how to extend this method to other graph recognition tasks, e.g. In this article, we have only considered the structural connectivity of edge nodes. The representation of structural connectivity between non-edge nodes is also worth exploring.

plain




 Paper Title: Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Review.



[
Unlocking Fair Use in the Generative AI Supply Chain: A Systematized Literature Review]

















Amruta MahuliMPI-SP
Asia BiegaMPI-SP












MPI-SP Max Planck Institute for Security and Privacy, Bochum, Germany



Amruta Mahuliamruta.mahuli@mpi-sp.org 
Asia Biegaasia.biega@mpi-sp.org

















  

§ ABSTRACT 
Through a systematization of generative AI (GenAI) stakeholder goals and expectations, this work seeks to uncover what value different stakeholders see in their contributions to the GenAI supply line. This valuation enables us to understand whether fair use advocated by GenAI companies to train model progresses the copyright law objective of promoting science and arts. While assessing the validity and efficacy of the fair use argument, we uncover research gaps and potential avenues for future works for researchers and policymakers to address. 




§ INTRODUCTION

One of the demands made and accepted on account of the Writers’ Guild of America (WGA) protests[Art. 72 of Memorandum of Agreement for the 2023 WGA Theatrical and Television Basic Agreement: <https://www.wga.org/uploadedfiles/contracts/2023_mba_moa.pdf>] included disregarding generative artificial intelligence (GenAI) outputs as source material while determining writing credits and compensation. This demand highlighting the importance of credit and compensation for creative content creators was also voiced by Mr. Ashley Irwin during his congressional testimony[House holds hearing to examine the intersection of generative AI and copyright law — 05/17/23. Retrieved June 3, 2024 from <https://www.youtube.com/watch?v=isTgXmzoaXc>]. He reasoned that Art 1 section 8(8) of the US Constitution[U.S. Constitution. Art. I, § 8(8): <https://constitution.congress.gov/browse/essay/artI-S8-C8-1/ALDE_00013060/>] granted rights to authors and inventors to promote Science and arts, thus calling for policies that would regulate AI companies to generate their model using the ‘3Cs: consent, credit and compensation’ for creative workers (creatives). 

On the other hand, GenAI companies like OpenAI[OpenAI. 2019. Comment by OpenAI Regarding Intellectual Property Protection for Artificial Intelligence Innovation. Department of Commerce, United States Patent and Trademark Office. Retrieved December 22, 2023 from <https://www.uspto.gov/sites/default/files/documents/OpenAI_RFC-84-FR-58141.pdf>] are advocating for training models with copyrighted material as fair use based on how ‘transformative’ GenAI outputs will be, relying on Campbell v. Acuff-Rose Music[Campbell v. Acuff-Rose Music, 510 U.S. 569 (1994)] where the Supreme Court averred that “...the goal of copyright, to promote science and the arts, is generally furthered by the creation of transformative works.” 

Both arguments stem from the goal and justification for intellectual property law: to promote science and arts <cit.>. Ideally, these justifications form two sides of the same coin. However, in this case, and many lawsuits filed by creatives, they stand on opposing ends[Andersen et al v. Stability AI Ltd. et al, Docket No. 3:23-cv-00201 (N.D. Cal. Jan 13, 2023):  <https://docs.justia.com/cases/federal/district-courts/california/candce/3:2023cv00201/407208/67>][Main Sequence, Ltd. et al v. Dudesy, LLC et al, Docket No. 2:2024-cv-00711(Cal. Jan 25, 2024):  <https://s3.documentcloud.org/documents/24377081/carlin-lawsuit.pdf>][Tatjana Paterno and Layna Deneen. 2024. AI Threats Emerge In Music Publishers’ Battle With Big Tech. Hollywood Reporter. Retrieved June 3, 2024 from <https://www.hollywoodreporter.com/business/business-news/ai-threats-music-publishers-big-tech-1235767692/>][Blake Brittain and Blake Brittain. 2024. Microsoft, OpenAI hit with new lawsuit by authors over AI training. Reuters. Retrieved June 3, 2024, from <https://www.reuters.com/legal/microsoft-openai-hit-with-new-lawsuit-by-authors-over-ai-training-2024-01-05/>][Winston Cho. 2024. Scarlett Johansson, OpenAI Voice: Lawsuit for Actors Threatened. Hollywood Reporter. Retrieved June 3, 2024 from <https://www.hollywoodreporter.com/business/business-news/scarlett-johansson-ai-legal-threat-1235905899/>]. On the one hand, the creatives’ argument grounds itself in Lockean rhetoric of one's moral right to reap the benefits of one's labor. Conversely, OpenAI employs utilitarian rhetoric that views copyright as an incentivization system to enhance public welfare and advancement<cit.>. 

The challenge for the judiciary and the policymakers lies in how they ideologically interpret these copyright law arguments, given that they both hold ground. In addition to this challenge of interpreting cogent arguments, economically, in 2021, the core copyright industries in the US added a value of $1.810 trillion, which was 7.76% of the U.S. GDP <cit.>. Additionally, employment in the total copyright industries was more than 9.6 million creative workers, representing almost 5% of the total US employment <cit.>, making balancing this thin line of authorship rights and advancing cultural and scientific progress in society even more difficult. 

Therefore, this systematization of existing user studies draws from the conceptual framework of value similarity to corroborate the objectives of copyright law and the fair use doctrine for training GenAI models with the attributes valued by the stakeholders in their contributions to the GenAI supply chain. This paper begins by providing an overview of US copyright law and the philosophical groundings of copyright law. This section also maps the GenAI supply chain with the relevant stakeholders across every stage of the GenAI supply chain. Section 3 describes the method used in this paper to enable the analysis in Section 4. Section 5 discusses the results of the study. With the hope of uncovering research gaps and new future research directions, Section 6 concludes by reviewing the findings and proposing immediate computational challenges for better protection and implementation of the existing intellectual property frameworks, particularly in the context of GenAI.






§ METHODS



 §.§ Data Collection

This systematic literature review follows the PRISM statement while synthesizing existing Human-Computer Interaction (HCI) literature in English within the last 10 years since 2014 from 01/01/2014 to 04/30/2024 <cit.>.

We began by mapping stakeholders to different stages of the ML supply chain by relying on the supply chain conceptualized by Lee et al. <cit.> as a structuring framework. Given the complexity and possibilities of involved actors, Lee et al. <cit.>, for the sake of structure and clarity, our mapping of the stakeholders is approximate and has been summarized in Table 1.
 

We experimented with different keyword combinations based on our review goals and research questions to identify search query combinations to retrieve relevant existing literature. Given that our research questions seek to gauge the elements of value in the stakeholders’ contributions and ownership perceptions, we generalize by using different synonyms of the identified search strings where the star symbol (*) represents a wild card character, for example, recruit* could mean recruited or recruitment or recruit. We yielded 5895 papers using the following 4 sets of search strings for our search:

    
  * [Search across the paper for: "writer*" OR “knowledge workers” OR author* OR storyteller OR novelist* OR poet* OR co-author* OR “coauthor*” OR “cocreator” OR “co-creator” OR “creator” OR “artist” OR “dataset curat*” OR “database curat*” OR “dataset creat*” OR “database creat*” OR “ML practitioner*” OR “developer” OR “software developer” OR “ML developer” OR “computer scientist*” OR “data scientist*” OR “UX practitioner*” OR “UX designer*” OR “user*”] AND [Search within the abstract for: "user studies" OR "survey" OR "interview*" OR "recruit*"] AND [Search across the paper for: "goals" OR "expectation*" OR "own*" OR "incentiv*" OR "motiv*"]
    
  * [[Search across the paper for: "designer"] AND [Search with the abstract for: "user studies" OR "survey" OR "interview*"] OR "recruit*"] AND [Search across the paper for: "goals" OR "expectation*" OR "own*" OR "incentiv*" OR "motiv*"] AND [Search across the paper for: "creativ*" OR "ux"]]
    
  * [[Search across the paper for: “GenAI" OR "generative artificial intelligence" OR "generative AI" OR "generative output"] AND [Search with the abstract for: "user studies" OR "survey" OR  "interview*"] OR "recruit*"] AND [Search across the paper for: "intellectual property" OR "copyright" OR  "patent"] OR "law"]]
    
  * [[Search across the paper for: "stakeholder*"] AND [Search within abstract for: "user studies" OR "survey" OR "interview*" OR "recruit*"] AND [Search across the paper for: "goals" OR "expectation*" OR "own*" OR "incentiv*" OR "motiv*"] AND [Search across the paper for: "machine learning" OR "ML" OR "AI"] OR "artificial intelligence"]]


To be eligible for our study, the publications met the following criteria:

    
  * Articles must be peer-reviewed full papers that were published over the last 10 years, limiting the results to the first 100 searches. Given the dynamic and fast-paced innovation in the machine learning and artificial intelligence domain, we believe that reviewing peer-reviewed full papers from the last 10 years will allow us to synthesize an understanding of the stakeholder perspectives.
    
  * The selected articles must be full research papers that used qualitative user interviews as a research method for their research. To gauge stakeholder perspective on subjective issues such as valuation and expectations, we believe that user interviews and survey-based studies and papers have the potential to act as surrogates of authority to gain information from stakeholders. Therefore, we did not include full papers based on quantitative methods, non-user studies papers, posters, doctoral theses, books, book chapters, panel discussion reports, abstracts, or work-in-progress papers.
    
  * The papers must relate to user perspectives on the thought process or workflow processes for generating works of authorship or generative artificial intelligence. Alternatively, we include papers relating to stakeholders’ perceptions of ownership regarding their contributions.





Based on these criteria, out of the 5895 yielded papers, papers that did not meet the criteria, duplicates, and full papers that could not be found were removed to result in 855 papers. Given that this paper is a scoping study for the systematic literature review of these 855 papers, it restricts its scope. It systematically reviews papers from the past 3 years, from 2022 to April 2024, resulting in 338 papers.  A more thorough manual elimination process using title and abstract analysis was conducted to ensure that only the most relevant papers were reviewed. This round of elimination resulted in 56 papers selected papers. 31 papers were randomly selected for this systematic scoping literature review. After reviewing the selected papers, 10 were eliminated as they did not meet the inclusion/exclusion criteria mentioned in this section, resulting in 20 full papers forming the final dataset.



 §.§ Data Analysis

Our units of analysis were the first 100 peer-reviewed, published full papers that came through the search as mentioned earlier queries, inclusion, and exclusion criteria from the ACM Guide to Computing literature library. We then analyzed the final filtered papers using MaxQDA 24, a qualitative and quantitative data analysis software. We used descriptive and in-vivo coding techniques to code the filtered papers for the following research questions: What do stakeholders in the GenAI supply think they value in their contributions to the GenAI supply chain?
We used affective methods such as in-vivo coding and value coding<cit.> by assigning codes to attributes that interviewed stakeholders, in their interview responses, mention or specify as incentivized or are considered ‘good’. The assigned codes were then analyzed using versus coding as an inspiration with labor theory and utilitarian theory as dichotomous groups.
Although we paid particular attention to the findings/results sections of the filtered papers to capture the interviewed stakeholders’ first-hand perceptions, we also carefully read through all the sections where stakeholders or the authors of the filtered papers mentioned any valuation or ownership perceptions.



§ RESULTS

Using value coding and in-vivo coding, we identified over 600 codes, structured into 8 groups. The grounding for each theme with each stakeholder group is illustrated in Fig. 2:

It must be noted that some of the identified themes do not correspond directly to our research questions. However, it is indicative of a successful thematic analysis that all the identified themes do not simply correspond to the research questions <cit.>.  We have not collected and analyzed any descriptive statistics or metadata of the selected reviewed papers other than those required in the search terms, and inclusion and exclusion criteria.



What do stakeholders in the GenAI supply think they value in their contributions to the GenAI supply chain?


 §.§ Value added by creatives while creating expressive work

Interpersonal communication and sense of community: 
Some creatives believe they add value when their expressive work enables interpersonal communication and a sense of community. The text-to-image artist in one study <cit.> claimed that discovery (of new concepts) and a sense of community is rewarding for them as an artist:  “Everything is more fun when you can share it.” This emphasis on communicating ideas is also reflected in other studies<cit.> emphasizing the human need to communicate and share ideas:  “We are humans. We want to communicate others how we feel, what ideas we have and that’s what it is. It’s an accelerator for human ideation and being able to reach.” In addition, participants in one study <cit.> highlight that their expressive work is a medium to “maintain closeness with their audiences across platforms”. 

Impact and activism: 
Some creatives believe that the value of expressive work lies in the impact and activism it inspires in the artists and the public. One of the participants in the study<cit.>  noted that creating expressive work is  “not just having fun ... but [actually] making something that has some power.” A participant from another study<cit.> interviewing creatives on TikTok stated, “And then January 6, happened. I was like, I understand this intricately. As someone who has studied white nationalism–as someone who has studied insurrections and fascism. Part of what I want to do is educate people to keep themselves safe. And every now and then I post book updates, every now and then I post [anonymized] content. But now it’s mostly news. It’s mostly talking about data transparency; it’s mostly talking about keeping people safe from threats they didn’t even know existed.” 

Vulnerabilities and lived experience: 
Interviews in studies show that creatives add value by tapping into their vulnerabilities and lived experiences to create expressive works. Simpson et al<cit.>, in their paper, noted that  'scripts, films, and produces videos on TikTok about surviving suicide, the intention behind creating these videos is not to receive attention and acclaim, but to help cope with their grief and help others cope with theirs.' A creative in the same study<cit.> noted that “just being yourself” garners more success. A writer interviewed for a paper<cit.> to understand feedback networks emphasized the importance of the lived experience of their peers as an important factor in dictating whom the writer would ask for feedback: 
 "Since my brother is also Indian if I want to know how something reads to another Indian person, I will show him. But then if I’m writing a story about girlhood, I’ll send it to my friend Jen, who also writes about girlhood."
 Another text-to-image artist in the same study<cit.> noted that “Art doesn’t live in a vacuum, nobody starts from scratch, everything is based on something else.” 

Expertise, ideation, and implementation of creative ideas: 
Expertise, ideation, and implementation of creative ideas bring value to creatives' contributions while creating expressive content. Participants in a study<cit.> consider the artist’s background and education relevant when using text-to-image generation in the creative process. They state that:  “artistic intention is required to guide the interaction, prompt engineering benefits from knowledge of art history styles and artistic techniques, and assessing the system’s output requires an intuitive understanding of aesthetics and rules to achieve e.g. color harmony.”  A virtual influencer interviewed in a study<cit.> noted that being a virtual influencer is more accessible because  “you don’t have to be pretty to be a Virtual Influencer, just smart and be a good storyteller.”  Another artist in a study<cit.> shared:  "As a professional artist, shading, lighting, and the process after flatting is an eminent way to express my uniqueness. If I see the new features [that automate shadowing and lighting], I may feel an occupational crisis"  thus, emphasizing the importance of skill and creative imagination. A creative interviewed in a study focussed on multi-platform content creation<cit.> stated that he  “understood that his audiences across platforms have different interests, so he curated different granularity of gaming topics of video content for each of his channels” showcasing his niche by curating and implementing ideas into different kind of content for his many channels.



 §.§ Value added by ML practitioners while developing ML models

Managing end-users needs: 
ML practitioners in a study<cit.> shared that  "user needs to be the basis of the entire design.”  In another study<cit.>, participants corroborated this requirement when they stated that  “the success of the deployment does not include satisfying the functional or performance requirements but also how it is satisfying the target users.”  However, in the former study<cit.>, some participants expressed their trust in their abilities to map user needs, and some participants noted that information about the user and their needs is often collected by conversing with the client and not the users. This paper<cit.> also stated that in 9 out of the 12 companies, the technical AI development team maps users' needs. In the remaining 3 companies, UX designers were responsible for mapping user needs.

Documentation and explainability: 
Only one ML practitioner in the reviewed literature<cit.> shared that they also need to “version each model and be able to connect them to datasets they were trained with”.

Expertise and ability to meet clients’ needs: 
ML practitioners add value to the ML supply chain through expert domain knowledge and their ability to meet clients’ needs. A participant in a study<cit.> noted that  “In addition to the AI development, AI teams are responsible for the need assessment, early-phase interaction design, and communications with the client.”
 Although companies often either built their models in-house or used third-party models<cit.>, model development, itself, was considered a highly iterative process where 'a lot of time is spent on tweaking the model parameters to achieve best possible performance.'<cit.> 8 out of 12 participants in a study<cit.> noted that they do not have established processes when designing a model. Due to AI's uncertainty and dynamic nature, the ways of working strongly depend on the data, product, used AI technique, and the client<cit.>.  A participant in another study<cit.> also corroborates this uncertainty when they expressed: "When [the ML model is] multiplying enormous matrices together and then applying some non-linear functions to them a hundred times in a row, how can anyone ultimately know what is happening there?" 



 §.§ Value added by UX practitioner while developing ML models’ interface

Catering to end-users needs:
Interviews with some ML practitioners in the reviewed study<cit.> indicate that UX practitioners are responsible for managing users’ needs: an ML practitioner noted, “If we talk about the end-user, UI is the way to answer their needs. It’s hard for me to come up with any user-related practices during the early phase of development.”  
 These needs include ensuring simplicity, ease of use, and easy-to-test interface for AI models<cit.>. A UX practitioner in a study<cit.> highlighted the importance of simplicity and ease of testing while designing the proof of concept for an interface, he noted:  “We probably want to start with something minimal that we can actually test with users.” 

Accuracy, flexibility, and explainability of ML models:
The weight of user needs to ensure the accuracy, flexibility, and explainability of ML models falls upon the UX practitioners. A survey<cit.> studying the collaborative practices and tools of UX practitioners noted that accuracy and flexibility were two common goals for UX practitioners. When comparing the workflow for designing AI and non-AI users, the participants in the same study<cit.> noted that although the workflow for both user interfaces was not that different, they had to design to ensure user consent and awareness:  “When pure UX is concerned, I don’t see it as significantly different from any other user experience. I think that the user consent and awareness [of AI being used] needs to be a little bit more, but apart from that, in terms of experience, it needs to be like anything else I guess.” 

Designing, creating, and editing content:
Similar to creatives, UX practitioners add value to the ML supply chain by designing, creating, and editing content by applying their domain knowledge and expertise. UX practitioner participants in a study on text-to-image AI use<cit.> noted that GenAI allows them to side-step their artists when they are too busy, unavailable, or when their engagement is too time-consuming:  "We definitely can use just straight-out stuff from Midjourney and give them to an artist instead and they’ll be super happy now. This is a good idea and they can, like copy, paste and basically paint over stuff then maturing, and then we can use that already.".  In addition to creating and editing content, interviewees in a study<cit.> described several other considerations while designing an interface that highlights and applies their expertise which includes  “how the language of the VUI is ‘built’ i.e., its ‘components’, how content is delivered, and how temporality (e.g., length of speech) shapes design (P8). Interviewees argued that language components such as syntax, grammatical rules, semantics, and lexical morphology dramatically influence the user experience of voice interfaces (P6) and therefore provided a go-to set of reflective considerations during VUI design.”  



 §.§ Value derived by users from GenAI outputs:

Content that yields joy and entertainment:
Users interviewed in the reviewed studies noted that they value expressive content that yields enjoyment. A participant in a study<cit.> on blind visual art patrons noted,  “Exploring art is simple to me, I feel relaxed and connected to the environment while I am in art galleries. It is very similar to watching a movie or playing a game...”   
 For some patrons in the same study<cit.>,  “The goal for me to enjoy art is to understand the story and cultural background of different art pieces from different periods. Paintings and pictures are very language independent that you do not need to understand the language that the artists speak to appreciate their paintings..”   

Activism and messaging of expressive content: 
In addition to enjoyment, participants in another study<cit.> enjoyed expressive content for the activism and message of the expressive content. It must be noted that this study sought to understand the relationship between end-users and virtual influencers where some end-users stated that they followed the virtual influencer for  “the message they give, the art style, and mostly the stories they’re communicating."  Another participant in the same study<cit.> liked the positive message on environmental sustainability that the virtual influencers created. Some participants in this study benefitted from the endorsement that the virtual influencers advertised noting  "I’ll also pay more attention to the endorsement and likely form an opinion about the endorsing company that this must be a ’cool’ and ’in touch’ company for having an awareness of this.”     

Content that is novel, useful, and non-obvious: 
Interviewees in a study noted that the participants characterized good ideas as having at least two of the properties of novelty, usefulness, or nonobviousness (but not always all three)<cit.>.  Participants in another study<cit.> surveying virtual influencer followers validated the importance of novelty and nonobviousness:  “The limitations that traditional influencers face is simply the fact that they are human. As an actual living being, there are certain boundaries to the things they can do. On the other hand, a virtual influencer is just the creation of a human. It can and will do anything one can dream of. This makes their content unique, be it Reels, posts, or stories.". Another participant in the same study<cit.> noted,  “Maybe because I know they are fake, and someone is working really hard to make a fake person??? It’s like the "artist" or "creator" or whatever of the virtual influencer is showcasing their abilities/progression. Like an artist would, posting their art.” 
; A participant in the same study<cit.> stated  “Virtual influencers are liked for their aesthetic. Being virtual is sort of part of their thing.”

Connecting with the persona and interpersonal relationship with the creator: 
While being virtual adds value to the creativity of virtual influencers, it also causes some of the participants to be emotionally detached from them, thus enabling them to engage more confidently with the influencers<cit.>. One participant in a study<cit.> noted that  “With a VI account, I comment more easily because it’s lighter, I never comment on other accounts because I figure that stars or other celebrities can read and I don’t necessarily seek more interaction with them. With a VI account, I don’t really think about the person behind the account because I figure the person is more detached, for bee influencing for example it’s just a small part of their job I guess.” This detachment and sense of anonymity, however, renders a greater responsibility on the creatives as users believe<cit.> that such creatives can voice their opinions more freely and engage on more sensitive issues because they are not human and therefore do not have to worry about repercussions such as public hate:  “I think that, if she would live in our world and she is up to date about things happening in our society, she would probably give an unfiltered and pure opinion because she is not a real person. She will be able to tell things a normal person wouldn’t want to say. . .if you can use an AI to say things humans are going to get canceled for then why not. . . Social media is very toxic and people can be hated based on their opinion. Wouldn’t it be great to use AI for sensitive topics?”



§ ANALYSIS AND DISCUSSION



 §.§ Does copyright law protect the attributes valued by stakeholders in GenAI supply chain?



For our analysis, we rely on the concept of value similarity introduced by <cit.>. In <cit.> paper, a simulation experiment imitating the value similarity model proved that similarity in values or attributes can be a driving force for cooperation and trust in the regulation and design of public policy. Given this context, we attempt to understand the value similarity between the values behind exclusive rights enshrined by copyright law and the attributes valued by stakeholders in the GenAI system, a summary of which can be found in Table. 

Creatives’ value addition in terms of expertise, novelty in ideation, and implementation of those ideas are protected under copyright law and are appreciated by end-users consuming the implementation of these ideas. Both creatives and end-users appreciate enjoyable, impactful content that promotes advocacy. Given the expressive nature of this type of content, it is protected under copyright law. 

Creatives’ value additions by doing work that fosters interpersonal communications with their fans/followers and a sense of community are also appreciated by end-users. However, these attributes are not protected by copyright law. Creatives also add value when they share their life experiences and vulnerabilities with the end-users. However, this kind of input is not protected by copyright law.

ML practitioners make a distinction between end-users' needs and clients’ needs. They add value to the ML supply chain when communicating and meeting clients’ needs. Their work also adds value to the supply chain through their expertise and ensuring adequate documentation and explainability of the model. Although ML practitioners’ expertise is protected through the models, they develop along with their work process of documenting and ensuring the explainability of the model through the document itself, their ability to communicate with the clients and meet the clients’ needs remains unprotected under copyright law.
The UX practitioners’ value addition in terms of meeting end-users' needs through the UX design and interface itself can be protected under copyright law, along with the contributions they make when they design, create, and edit content. However, their contribution when they ensure flexibility in the model accuracy and agility remains unprotected.  

Recommendations: Given this gap between the values protected under copyright laws and the attributes valued by stakeholders, this paper, therefore, calls for the need to reinvent copyright law with computational solutions that go beyond the letter of the law but encapsulate the spirit of the law. In particular, given the analysis of these studied papers, as future work, researchers may consider developing tools that offer a communal environment for fostering interpersonal communication and a sense of community with other stakeholders in the ecosystem. Researchers may also consider creating tools that enhance the GenAI infrastructure by supporting creative autonomy and providing nuanced technical means of self-expression while taking into account stakeholder needs. 



 §.§ Does the advocacy of fair use doctrine enable the progress of copyright law’s objectives?

The analyzed papers also show that ML practitioners believe that data processes such as training and fine-tuning models are the most challenging aspects of their workflow. With GenAI companies like OpenAI advocating for training models as fair use and reports suggesting that researchers may run out of data to train, it is pertinent to ask whether using the fair use doctrine to train GenAI models is, in fact, producing transformative content that is enhancing or even promoting the enhancement of public knowledge of science and arts.

OpenAI, in its submission to the Copyright Office [OpenAI. 2019. Comment by OpenAI Regarding Intellectual Property Protection for Artificial Intelligence Innovation. Department of Commerce, United States Patent and Trademark Office. Retrieved December 22, 2023 from <https://www.uspto.gov/sites/default/files/documents/OpenAI_RFC-84-FR-58141.pdf>], clarified that AI systems learn patterns from the training corpus and use those patterns to generate ‘novel’ expressive works which ‘share some commonality with works in the corpus subject to the model learning from ‘enormous number of works’ . Furthermore, OpenAI argues that such infringement “is an unlikely accidental outcome.” However, a report by the US Congressional Research Service[Congressional Research Service. 2023. Generative Artificial Intelligence and Copyright Law. US Congress. <https://sgp.fas.org/crs/misc/LSB10922.pdf>]  brings forth a contradicting narrative by the Getty Images lawsuit [Getty Images (US) Inc & Ors v Stability AI Ltd [2023] EWHC 3090 (Ch)] that alleges “Stable Diffusion at times produces images that are highly similar to and derivative of the Getty Images.” This report also brings attention to a paper<cit.> that found “a significant amount of copying” in less than 2% of the images created by Stable Diffusion where the authors claimed that their methodology “likely underestimates the true rate” of copying . While 2% may seem like a significantly small number, and it can be agreed that generative AI does have the potential to create transformative work, extant literature<cit.> indicates that ML practitioners who develop and train these models face difficulties predicting whether the outcomes of such AI models will be original and transformative for society at all:  “When [the ML model is] multiplying enormous matrices together and then applying some non-linear functions to them a hundred times in a row, how can anyone ultimately know what is happening there?” 

These characteristics of ML models being black boxes, their unpredictability, AI mimicry, and overfitting are also impacting the drive of creatives to create expressive works. In a survey of more than 1000 artists<cit.> where more than 95% of the artists post their art online, 53% of them anticipate reducing or removing their online artwork if they have not done so already. Out of these artists, 55% of them believe reducing their online presence will significantly impact their careers. One participant stated,  “AI art has unmotivated myself from uploading more art and made me think about all the years I spent learning art.” 78% of artists anticipate AI mimicry would impact their job security, and this percentage increases to 94% for the job security of newer artists. Further, 24% of artists believe AI art has already impacted their job security, and an additional 53% expect to be affected within the next 3 years.  This impact on creatives' jobs is also seen in the accepted demands of the WGA which highlights the precarious artistic and financial security in the creative industry due to the advent of generative AI models. 

The argument of fair use falls flat if we apply Leaffer's considerations for fair use doctrine<cit.>  which include a) that the use must not impose economic harm to the original copyright owners and b) that the use is one that provides something new and potentially valuable to society. Firstly, GenAI, in its present version, has raised significant apprehensions amongst the original creatives content creators regarding the economic harms they face due to GenAI outputs.  Secondly, it is evident from the extant literature that although GenAI has the potential to create transformative work, as of the submission of this paper, GenAI is still plagued with issues of AI mimicry, overfitting, and excess memorization. 

Recommendations: Therefore, unless these problems of AI mimicry, overfitting and excess memorization are resolved computationally, GenAI will continue to create pattern-based content that is neither original nor novel, failing to validate its claim for eligibility for the fair use exception.



 §.§ Conclusion

This systematic literature review reveals attributes that stakeholders in the ML supply chain believe are incentivized in their contributions. Although GenAI models and their outputs find their origins for intellectual property ownership in the contributions of the data curators, the lack of studies that focus on data curators’ perspectives in this regard highlights a gap in the existing literature. This paper also uncovers computational gaps that expose cracks in the GenAI companies' call for training data for GenAI models to be considered fair use. It also recognizes the need to reinvent and complement copyright law with computational solutions for better implementation of the letter and spirit of the law. It, therefore, spells out computational problems in GenAI models and outputs for researchers and practitioners that demand immediate resolution for better implementation and enforcement of intellectual property laws and governing ownership and stakeholder value beyond the protections afforded by copyright.

icml2024



















 Paper Title: Investigating Brain Connectivity and Regional Statistics from EEG for early stage Parkinson's Classification.



The Inevitable Quark Three-Body Force and its Implications for Exotic States
    Su Houng Lee
    August 5, 2024
============================================================================



§ ABSTRACT 
We evaluate the effectiveness of combining brain connectivity metrics with signal statistics for early stage Parkinson's Disease (PD) classification using electroencephalogram data (EEG). The data is from 5 arousal states - wakeful and four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost model for classification on a challenging early stage PD classification task with with only 30 participants (11 PD , 19 Healthy Control). Evaluating 9 brain connectivity metrics we find the best connectivity metric to be different for each arousal state with Phase Lag Index achieving the highest individual classification accuracy of 86% on N1 data. Further to this our pipeline using regional signal statistics achieves an accuracy of 78%, using brain connectivity only achieves an accuracy of 86% whereas combining the two achieves a best accuracy of 91%. This best performance is achieved on N1 data using Phase Lag Index (PLI) combined with statistics derived from the frequency characteristics of the EEG signal. This model also achieves a recall of 80 % and precision of 96%. Furthermore we find that on data from each arousal state, combining PLI with regional signal statistics improves classification accuracy versus using signal statistics or brain connectivity alone. Thus we conclude that combining brain connectivity statistics with regional EEG statistics is optimal for classifier performance on early stage Parkinson's. Additionally, we find outperformance of N1 EEG for classification of Parkinson's and expect this could be due to disrupted N1 sleep in PD. This should be explored in future work.






Parkinson's Disease, Classification, Time Series, Connectivity





§ INTRODUCTION
 
Parkinson's Disease is the second most prevalent neurodegenerative disease and is characterised by the loss of dopaminergic neurons in the substantia nigra <cit.> <cit.>. Extensive loss of these neurons culminates in movement-related symptoms including muscle stiffness, instability and a tremor.  However, prior to the development of motor symptoms, PD is very challenging to diagnose, complicating early detection and intervention.
The progression of the disease can be rated accorded to a clinical rating scale such as the HY (Hans and Yahr) scale <cit.>. This scale defines broad categories of motor function in Parkinson's and has 5 stages with stage 1 being the earliest. There is a need for earlier diagnosis of Parkinson's and Electroencephalogram (EEG)  has shown promise for this. EEG is non-invasive, relatively inexpensive and records the electrical activity of cortical neurons in the brain with a high temporal resolution.

Limited work has been done on the earliest stage (HY 1) Parkinson's classification. Limited work has also been done on the fusion of brain connectivity with signal statistics and exploration on whether the information in each is supplementary or redundant. Prior work on general PD classification uses methods such as deep learning (e.g. Convolutional Neural Networks), signal statistics input to a machine learning model and measures of brain connectivity (see related work <ref>) input to a machine learning model. Machine learning models used in this previous work include Support Vector Machines, Neural Networks and tree based models such as Random Forest and Ada Boost classifiers <cit.>.

In this work we evaluate the effectiveness of combining signal statistics with brain connectivity for early stage PD classification (mean HY 1.55) using EEG recorded across 5 arousal states. The arousal states include wakeful data and the four sleep stages (N1, N2, N3 and REM). We investigate 9 brain connectivity metrics individually and in combination with regional EEG statistics. Best classifier performance is achieved by combining brain connectivity with signal statistics from multiple brain regions. This result suggests that brain connectivity metrics and signal statistics provide supplementary information for classification of early stage PD and best performance is achieved by combining both.



§ RELATED WORK
 



 §.§ Parkinson's Classification with EEG

Prior work has found frequency based statistics of the EEG signal input to machine learning models to be effective for classification. We build on work regarding peak frequencies and band powers from EEG. Band powers refers to EEG signal strength within defined frequency ranges whereas peak frequency refers to the highest strength frequency <cit.>.  In <cit.> the authors achieve 78% accuracy on PD classification with a balanced data set using EEG band power values, median and peak frequency as features. In  <cit.> an accuracy of 86% is achieved with a support vector machine using frequency based EEG features. In  <cit.> the authors combine coherence (functional connectivity measure) with wavelet coefficients achieving an accuracy of 94%. More complex spectral features have also been used such as in <cit.> where authors achieve 99% accuracy using features of the third-order spectra of EEG (bispectrum). However, a limitation of this study is that participants were not in the early stage of PD - they were in stage II and III according to the HY scale. Other work such as  <cit.> achieved lower performance (85% accuracy) using complex spectral features. Deep Learning methods have also shown promise such as in <cit.> where the authors achieve 96.9% accuracy using a 'convolutional-recurrent' neural network architecture. To summarise, EEG classification of Parkinson's shows promise. However current work on early stage Parkinson's is limited and limited work has been done to explore different connectivity metrics across different arousal states and how performance changes when fusing brain connectivity information with regional statistical EEG features.  



 §.§ Brain Connectivity
 
Measures of connectivity across the brain provide valuable information about brain activity and are thus useful for EEG classification. Different types of connectivity include structural connectivity, functional connectivity and effective connectivity. Structural connectivity corresponds to anatomical connectivity between brain regions, functional connectivity characterises statistical dependencies or correlations in activity and effective connectivity explores causal or directional influences between regions<cit.>. A number of brain connectivity measures have been defined in the EEG literature <cit.>. For this work we explore 9 brain connectivity measures including  Coherence (coh) , Imaginary Coherence (imcoh) , Phase-Locking Value (PLV), corrected imaginary PLV (ciplv) , Pairwise Phase Consistency (PPC) , Phase Lag Index (PLI), Directed Phase Lag Index (DPLI), Weighted Phase Lag Index (WPLI) and debiased weighted phase lag index (DWPLI).  

For conciseness we define PLI in detail only as this metric was evaluated in most detail in our experiments (see section <ref>). Phase lag index measures the asymmetry in the distribution of phase differences over time between two signals. The PLI ranges between 0 and 1 with 0 indicating no coupling or instaneous coupling and 1 indicating that there is a true lagged interaction. By definition if the distribution of phase differences between two signals are given by Δϕ(t_k) for k = 1 … N then the phase lag index is given by <cit.> : PLI = | ⟨sign[Δϕ(t_k)] ⟩|] 

Connectivity metrics such as PLI can be calculated for two signals or for components of each signal. For example, one can calculate the PLI between the delta band (0.5-4Hz) of one signal with the delta band of another signal. In <cit.> the authors use Phase Lag Index to classify Mild cognitive impairment and find phase lag index in the alpha band to be correlated with a cognitive assessment score.

PLI as a connectivity metric has the benefit of being insensitive to volume conduction effects <cit.>. Volume conduction occurs when there is a single source of neuronal activity in the brain but it's electrical signal is measured simultaneously at multiple electrodes. This can lead to a false positive for connectivity between electrodes or brain regions. 




 §.§ Frequency based signal statistics
 
Frequency based statistics are commonly used in EEG classification pipelines. Power spectral density (PSD) refers to the distribution of power vs. frequency for a signal and is computed with the Fourier Transform. Research has shown the strength of the EEG signal in specific frequency power bands to be informative of brain activity. These power bands are defined as delta (0.5–4Hz), theta (4–8Hz), alpha (8–12Hz), sigma (12–16Hz), beta (16–30Hz) and gamma (30–40Hz) <cit.>. The peak frequency (highest power frequency) in the PSD aswell as the power in that frequency are also informative statistics for classification. For longer EEG signals (over 5 minutes) enhanced classification performance can be achieved by calculating distributions of these frequency based statistics over time windows across the signal. <cit.>





§ METHOD



The data used in this work is from a yet unpublished dataset containing 57 channel wakeful and sleep EEG from Parkinson's and Healthy participants. The data set is described in further detail in <ref>.



 §.§ Preprocessing

The EEG data for each participant was scored manually in 30-second epochs in line with AASM guidelines <cit.>.  Epochs were labelled as Wake, N1, N2, N3 or REM. The EEG data was downsampled from the recording frequency of 512Hz to 256Hz and a 0.25 - 40Hz bandpass filter applied. Next, Independent Component Analysis and manual inspection of the EEG signal and power spectral density was completed for further artefact removal. Bad channels were interpolated and the data re-referenced using the REST referencing technique <cit.>.



 §.§ Regional Statistics Classifier
  
For the regional classification pipeline the 57 EEG electrodes were grouped into 13 brain regions of interest (see figure <ref>) . 



Per electrode and per region, statistics of 15 key frequency based features were calculated. These 15 statistics were the 6 band powers as defined in <ref>, total signal power aswell as the four peak frequencies and their corresponding power values. These 15 statistics are calculated per 6 second window resulting in 15 feature distributions. Key statistical features of these feature series including length, maximum, absolute maximum, minimum, mean, median, standard deviation, variance, root mean square and sum are calculated and input to a machine learning model (Ada Boost). The pipeline is shown in figure <ref>.




The statistical model is fit using features from single brain regions, two brain regions together at a time and all brain regions together.



 §.§ Connectivity Classifier
 
For the connectivity classification pipeline. Connectivity is calculated for every electrode-electrode pair. For 57 electrodes, this results in 1596 features calculated per sample. An example electrode-electrode connectivity grid is shown in Figure <ref>. The x-axis and y-axis contain the electrode names and between each electrode a connectivity value is calculated. The grid is symmetrical as electrode 1 connectivity with electrode 2 is the same as electrode 2 connectivity with electrode 1. The diagonal is blank as the connectivity of an electrode is not calculated with itself.



The calculated connectivity features are input to a machine learning model (AdaBoost) for classification. This is done for 9 connectivity metrics as outlined in experimental procedure (section <ref>).





 §.§ Combined Classifier

The combined classifier simply combines the regional statistical features with brain connectivity features for classification. Both sets of features are input to our machine learning model (Ada Boost classifier).



§ EXPERIMENTS AND DISCUSSION





 §.§ Data
 
The dataset includes 57-channel EEG data for 19 Healthy Control and 11 Parkinson's participants.  The data includes wakefulness EEG and recordings of the four sleep stages (N1, N2, N3, REM). In home recording was conducted on 2 or 3 nights for all participants in line with the AASM standards for polysomnography  <cit.>. The demographics of PD and Healthy participants are similar, see table <ref>



The number of samples exceeds the number of participants as EEG data was recorded on multiple nights. Those recordings of a low quality were dropped. Samples per class per arousal state aswell as mean sample length in epochs (where an epoch is 30s for sleep data and 20s for wakeful) are shown in Table  <ref>.





 §.§ Experimental Procedure
 
All experiments were run with two random seeds using 5-fold cross validation. The mean across the two sets of 5-fold cross validated experiments was calculated to determine the classifier performance. Where multiple samples were present from a single participant they were kept in the same fold to avoid information leakage. 

The regional statistics classification pipeline (see <ref>) was deployed using features from each of the 13 brain regions individually, using features from two brain regions at a time (78 combinations) and using features from all 13 brain regions at once for each arousal state. 

The connectivity pipeline (see <ref> ) was deployed for 9 connectivity metrics for each arousal state. Each connectivity metric was calculated for each band power delta (0.5–4Hz), theta (4–8Hz), alpha (8–12Hz), sigma (12–16Hz), beta (16–30Hz), gamma (30–40Hz) and for the entire EEG signal. Thus there were 7 results for each connectivity metric for each arousal state. The 9 metrics used in the pipeline were Coherence (CoH), Imaginary Coherence (Imcoh), Phase-Locking Value (PLV), corrected imaginary PLV, Pairwise Phase Consistency (PPC) , Phase Lag Index (PLI), Unbiased Phase Lag Index, Directed Phase Lag Index (DPLI), Weighted Phase Lag Index (WPLI) and debiased weighted phase lag index (DWPLI).  

Finally, a combined pipeline was deployed for the best performing overall connectivity metric combined with each set of statistical features for each arousal state.



 §.§ Results
 

The regional statistical features pipeline had best performance using features from two brain regions at a time. We present results from this best performing statistical approach. For this classification method a best accuracy of 78.3% was achieved on Wakeful data and 77.5 % accuracy on  N1 data. Worse performances (less than 65 % accuracy) were achieved on N2, N3 and REM data (see column 2 in table <ref>).

The best performing connectivity metric was Phase Lag Index on the gamma power band using N1 data which achieved a performance of 86.2% classification accuracy. Across other arousal states other metrics performed better. Table <ref> below shows the best performing metric per arousal state with their corresponding performance. 




With PLI as the highest performing connectivity metric on an individual arousal state (86.2 % N1) we combined PLI with statistical features across arousal states. Results are in table <ref> below. The table also shows results for the best individual PLI only models and best statistical features only models to highlight the effect of combination.





  §.§.§ Analysis

Per table <ref> the best performing connectivity metric varied per arousal state. The best overall performance was with N1 data where Phase Lag Index on the gamma power band achieved an accuracy of 86.2%. Phase Lag Index measures the consistency of phase differences between two signals and is insensitive to volume conductive effects (see <ref>). Other metrics derived from PLI include Weighted Phase Lag Index which de-weights the value of small phase differences and the debiased estimator of squared weighted phase lag index (DWPLI). With PLI having highest performance for N1 this suggests that volume conduction effects are likely to be present in the data as well as true lagged interaction at small phase differences. This would explain why WPLI and the debiased estimator of squared WPLI do not outperform PLI on N1 data. Across other arousal states different metrics perform best. This suggests that the patterns of connectivity discriminating Healthy Controls from those with Parkinson's likely varies across arousal states. This should be explored in further detail for future work. 



Table <ref> shows per arousal state the best accuracy achieved with using Statistical Features only, Connectivity (PLI) only and Statistical Features combined with Phase Lag Index. For every arousal state combining statistical features with PLI leads to an increase in mean accuracy. This suggests that the statistical features and PLI provide useful complementary information for classification of Parkinson's. Therefore if performance is the main priority over computational efficiency then one should always include connectivity data to complement statistical features for classification. 

The overall best performing classifier on N1 data used Statistical Features combined with Phase Lag Index. This model achieved an accuracy of 91 %. For the classification of Parkinson's disease precision and recall are also important. This model achieved a recall of 80 % and precision of 96% (see figure <ref> for confusion matrix).  



This demonstrates the effectiveness in combining connectivity and statistical features. This also indicates N1 data as being very discriminatory for this classification task. This could be due to the Parkinson's disease participants having more disrupted light sleep during the N1 phase and should be explored in future work.









 §.§ Future Work

Future work should explore the differences between connectivity metric performance across data types for Parkinson's classification. This could help to highlight the differences in the Parkinson's brain activity vs Healthy controls across wakefulness and the sleep stages. 

Secondly, future work should work on optimal methods for fusion between connectivity metrics and statistical features. In this work we used simple feature concatenation (feature level fusion) to demonstrate the effect of using the respective features individually and together. For optimal classifier performance data fusion techniques such as late fusion (fusion at the decision level), model level fusion (at the representation level) or a hybrid between methods can be implemented <cit.>.

Finally, work should be expanded to other data sets as our data set is relatively small with 30 participants (19 Healthy Control, 11 Parkinson's). Replication of our results should be done on additional data sets to show generalisability of the result.




§ CONCLUSION
 

We evaluate the effectiveness of EEG signal statistics and brain connectivity metrics across 5 arousal states (Wakeful, N1, N2, N3 and REM) for early stage Parkinson's classification. Brain connectivity metrics are evaluated on their own and in combination with regional signal statistics using feature level fusion. Phase Lag Index on the gamma band power on N1 data was found to be the highest performing connectivity metric (86 % accuracy) with different metrics performing best on each arousal state. Phase Lag Index was then combined with signal statistics across all arousal states. The combination of PLI with regional signal statistics increased mean accuracy of the classifier across every arousal state. This suggests that fusion of connectivity with regional signal statistics is important for best performance. The best overall classifier (PLI and regional statistics combined) was on N1 data achieving 91 % accuracy, 96% precision and 80% recall. Future work should explore how connectivity metric performance varies across arousal state and optimal methods for fusion of EEG signal statistics with brain connectivity information.







IEEEbib




 Paper Title: AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation.






Augmented Masked Autoencoder Pretraining for 3D Brain Segmentation






A. Munk et al.




Department of Computer Science, University of Copenhagen, Copenhagen, Denmark Pioneer Centre for AI, Copenhagen, Denmark 

{asmu,jaam}@di.ku.dk
Equal contribution



: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation
    Asbjørn Munk1,2* Jakob Ambsdorf1,2* Sebastian Llambias1,2 Mads Nielsen1,2

    August 5, 2024
==============================================================================================



§ ABSTRACT 
This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset. We introduce , a dataset of 44,756 brain MRI volumes from public sources, the largest public dataset available, and revisit a number of design choices for pretraining modern segmentation architectures by simplifying and optimizing state-of-the-art methods, and combining them with a novel augmentation strategy. The resulting AMAES framework is based on masked-image-modeling and intensity-based augmentation reversal and balances memory usage, runtime, and finetuning performance. Using the popular U-Net and the recent MedNeXt architecture as backbones, we evaluate the effect of pretraining on three challenging downstream tasks, covering single-sequence, low-resource settings, and out-of-domain generalization. The results highlight that pretraining on the proposed dataset with AMAES significantly improves segmentation performance in the majority of evaluated cases, and that it is beneficial to pretrain the model with augmentations, despite pretraing on a large-scale dataset. Code and model checkpoints for reproducing results, as well as the  dataset are available at <https://github.com/asbjrnmunk/amaes>.









§ INTRODUCTION





Large-scale self-supervised pretraining promises immense potential for medical image segmentation. Works in computer vision from the natural image domain demonstrate that state-of-the-art results are achieved by scaling model capacity and pretraining on large datasets, enabling increased generalization performance, few- or zero-shot predictions, and highly semantic embeddings <cit.>. 


To enable self-supervised representation learning in vision, various methods have been proposed. Early works involved reversing image degradations, such as denoising images <cit.>, solving jigzaw puzzles <cit.>, or re-colorizing images <cit.>.
Masked-Image-Modeling (MIM) proved to be an especially successful and scalable variant, where patches of the input image are masked and learned to be reconstructed either directly or indirectly by predicting an embedding <cit.>. A Masked Autoencoder (MAE) is an encoder-decoder architecture that reconstructs the entire image from a masked version <cit.>.
Another family of approaches applied contrastive learning, where semantically similar transformed inputs are mapped closely in a latent space, while dissimilar inputs are pushed apart <cit.>. Self-distillation is based on a related idea, enforcing similar embeddings for semantically close inputs for a student and teacher network <cit.>.
Recent approaches combined these image-level contrastive or self-distillation objectives with patch-level MIM to enhance the spatial representation performance for downstream segmentation tasks <cit.>.

Evidence from medical vision indicates that the benefits of pretraining are transferable to biomedical image segmentation <cit.>. Yet, the current method of choice for solving segmentation tasks is still a fully-supervised paradigm <cit.>. Due to a heavily optimized data augmentation pipeline, the fully-supervised approach can be effective even with limited labeled data <cit.>. However, to fully leverage the potential of large-scale models in medical image segmentation as witnessed in computer vision, more training data is required, and the supervised approach is inherently limited by labeling cost. Large-scale unlabeled medical imaging datasets could resolve this need, but they are not readily available to the public, hindering progress for methodological research on how to harness unlabeled data for downstream segmentation most effectively. To address the need for pretraining datasets in the brain MRI domain, we propose , to the best of our knowledge the largest public pretraining dataset for brain MRI to date[  is an order of magnitude larger than the pretraining dataset used in SwinUNETR, the current state-of-the art methodology.] comprising of 44,756 volumes collected from public sources.

Using this large-scale dataset, we are proposing the AMAES (Augmented Masked Auto Encoder for 3D Segmentation) framework for pretraining modern semantic segmentation architectures. AMAES combines the successful Masked Image Modeling (MIM) task <cit.> with augmentation reversal, both of which can be viewed as special cases of denoising autencoding  <cit.>, with the goal of learning local, detailed representations that are invariant to intensity shifts between different domains. The resulting framework balances factors such as GPU memory usage, pretraining runtime, and downstream task performance. We compare AMAES against the state-of-the-art pretraining method proposed for SwinUNETR <cit.>, which combines MIM with a rotation prediction task and a contrastive learning objective. We find that in practice, solving the rotation problem is trivial while the contrastive loss adds little benefit provided the relatively small batch sizes possible in 3D imaging. Our simplified framework results in a significant speedup and memory reduction while improving downstream performance.

We evaluate our method using two backbone architectures: (i) The U-Net, as the most popular semantic segmentation model, and (ii) MedNeXt, as a recently proposed modernization. The  dataset and proposed AMAES framework for pretraining segmentation models contribute to building a foundation for the next generation of large-scale biomedical image segmentation models. 







§  DATASET


We propose   a dataset of 44.756 Brain MRI volumes collected from public sources, featuring a diverse set of acquisition parameters and patient populations. The dataset is a compilation of data from four large non-labelled datasets (ADNI <cit.>, OASIS3 <cit.>, OASIS4 <cit.>, PPMI <cit.>) and five challenge datasets (MSD <cit.>, BraTS21 <cit.>, ISLES22 <cit.>, WMH <cit.>, MSSEG1 <cit.>). The resulting dataset is highly heterogeneous and assembled to simulate the characteristics of clinical data. Overview of the dataset can be found in Table <ref>. The dataset includes data acquired at both 1.5T and 3T. PPMI data is openly available from PPMI. ADNI data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. To compile the diverse data into a suitable pretraining dataset, we transformed all volumes to the RAS coordinates system, resampled to isotropic 1mm spacing, clipped values to the 99% percentile, and z-normalized on a volume level. Afterwards, each volume is cropped to the minimum bounding box of the brain. The preprocessing pipeline is based on the  framework for medical imaging <cit.>. Code to reproduce the final version of the dataset is provided in the project repository. Usage of the dataset needs to comply with the individual legal requirements of each source.




§ METHOD



In this section we briefly describe . Our approach is highlighted in Figure <ref>. The overall goal of the framework is to provide an effective and efficient pretraining strategy for large-scale datasets, requiring a balance between memory usage, runtime and downstream performance (see Figure <ref>).



 §.§ Pretraining


  is based on a Masked Autoencoder (MAE) architecture with a light weight decoder and a specific augmentation scheme. Importantly, the architecture assumes no specific choice of encoder and can be used with any encoder that is compatible with the skip connections of the U-Net decoder.

Pre-text task. Prior work has centered around 2D natural images <cit.>, however, previous work showed that a Masked Autoencoder works well in 3D by applying it to 3D CT Brains using the SwinUNETR architecture <cit.>. The SwinUNETR pretraining objective further included both a rotation and a contrastive loss. However we find that the rotation task was near-trivial for the models to solve and thus provided no tangible utility, while the contrastive loss resulted in the memory footprint during pretraining to be doubled, without significant performance gains (see Appendix <ref>). Threfore, we reduce the pre-text task for  to masked reconstruction, and instead increase the batch size and double the number of training epochs. For each 3D input patch, the input is divided into subpatches of size 4×4×4 voxels, and each subpatch is masked with probability 0.6, following prior work <cit.>.



Decoder architecture. Since the decoder is discarded after pretraining, it is desirable to use as small a decoder as possible. This not only decreases training time and memory usage, but also forces the network to learn expressive representations in the encoder.

We find that a light weight version of the standard U-Net decoder provides a good balance between flexibility, memory efficiency and parameter count. Table <ref> shows results of pretraining with two versions of the U-Net decoder: the standard 3D U-Net decoder and one with half the number of convolutional layers (denoted light weight decoder). Interestingly, the light weight decoder not only decreases pretraining time with 20%, but also further increases finetuning performance by 1.96 dice points.

Skip connections. While skip connections are used during finetuning, our proposed encoder-decoder architecture does not include skip connections during pretraining. We found that using skip connections during pretraining tended to slightly adversely affect downstream performance, with a finetuning dice score of 56.22 with and 56.65 without, on 6-fold cross-validation on the BraTS21 dataset. While the difference is not significant, we choose to not use skip connections in the pursuit of the simplest possible setup.

Augmentations. To learn representations which are robust to changes in the input domain, we apply an extensive augmentation pipeline to the data during pretraining. The full list of augmentations include all augmentations used in nnUnet <cit.> and is given in Appendix <ref>. We want our representations to be covariant to any spatial augmentations (rotation, scaling), while we seek to learn representations that are invariant to all other augmentations, for instance additive noise. We thus only apply spatial augmentations to the reconstruction target. This is visualized in Figure <ref>. Table <ref> shows the result of applying augmentations to the pretraining data: when applying augmentations during pretraining, we see a 1.24 Dice point increase in finetuning performance, compared to no augmentations, when finetuned with only spatial augmentations.



 §.§ Finetuning


For finetuning, we transfer the backbone encoder weights from pretraining and attach a new standard U-Net decoder with skip connections between the encoder and the decoder. We only use spatial augmentations (rotation, scaling) during finetuning, since this setup performed better empirically. Upon closer inspection, the learning curves showed signs of catastrophic forgetting during the second half of training when finetuned with the full augmentation package.









§ EXPERIMENTAL DESIGN



 §.§ Datasets

We evaluate   on three different segmentation tasks covering tumor (BraTS21 <cit.>), ischemic stroke lesion (ISLES22 <cit.>) and white matter hyperintensity (WMH <cit.>). To test the models few-shot learning ability, we evaluate the models in a low resource setting: we only use n_train = 20 labeled examples for finetuning, while we evaluate the models on the remaining data. We choose n_train = 20 to realistically model what one medical expert can manually label with limited effort. Since the pretraining dataset considers each sequence individually, we evaluate the model's ability to perform downstream tasks in a single sequence setup, and thus for BraTS21 and WMH we consider only the flair modality, for ISLES22 we consider only the DWI modality.

Hyperparameters were selected on 3-fold cross-validation splits of the BraTS21 dataset. We do not tune hyperparameters individually for each fold or for each dataset. All results are averages over 6 folds. For BraTS21, we evaluate the model on a 20% holdout set. For ISLES22 we follow <cit.> and provide results from the rotating cross-validation splits. For WMH we both perform cross-validation evaluation (denoted "WMH") and test set evaluation. The test set contains data from scanners not present in the training set, which are also not present in , and thus evaluates the models ability to generalize to unseen data. We call this dataset "WMH OOD" (out-of-distribution). 



 §.§ Configurations

Our baselines, pretraining and finetuning are implemented using PyTorch, PyTorch Lightning, and the   library for medical image analysis <cit.> as a back-bone. All models are trained using 16-bit mixed-precision to limit memory footprint. Models were trained patch-based, using an input patch size of 128 × 128 × 128. Models are pretrained for 100 epochs (one epoch is one patch from each volume) and trained supervised for 2500 steps on downstream datasets. The exact hyperparameters used for each model is given in Appendix <ref>. All models are pretrained on two H100 GPUs, and fine-tuned on a single H100 GPU.



 §.§ Baselines

We evaluate  along two dimensions: (i) How does   compare to the pretrained SwinUNETR?, and (ii) What is the impact of pretraining on downstream performance?

To evaluate (i), we pretrain SwinUNETR with the exact configuration given in <cit.> on , which includes both a contrastive loss, a rotation loss and a reconstruction loss, as well as a different choice of masking ratio and mask size. To ensure a fair comparison, SwinUNETR is pretrained for 100 epochs, similar to , and is pretrained with patch size 128^3, instead of patch size 96^3 and 90 epochs. To address (ii), we apply   to a set of convolutional backbones and evaluate the difference between training from scratch and finetuning the pretrained model. The backbone models include a U-Net in two sizes: XL (90M parameters) and B. The U-Net B is very similar in size to the one used in nnUNet (22M parameters, when trained with the default setting  of 12 GB). Further, we explore using the modernized U-Net architecture MedNeXt <cit.>, which uses depth-wise seperable convolution to introduce compound scaleable medical segmentation models. All MedNeXt models are trained with kernel size 3 and do not use UpKern. The networks are finetuned and trained from scratch using the same hyperparameters. Models trained from scratch use the full augmentation pipeline.







§ RESULTS AND DISCUSSION


The results of our experiments are given in Table <ref>. Both U-Net B, U-Net XL and MedNeXt models benefit from pretraining in all scenarios. MedNeXt M does not benefit from pretraining on BraTS21 and ISLES22, however exhibits very good performance on WMH both in and out of domain. We believe this is mainly due to strong baseline performance, before pretraining is added. Note that models do not use the full augementation pipeline during finetuning, while the baselines from scratch use all augmentations. All models trained with   outperform the pretrained SwinUNETR, except for the standard U-Net on WMH OOD. Generally, model size seems to be correlated with performance, with two notable exceptions: The SwinUNETR model seems to be worse than both MedNeXt M model and the standard U-Net, both of which have less than a third of the parameters of SwinUNETR. The results on WMH OOD demonstrate that pretraining also increases performance when applied to out-of-domain data – even with respect to the pretraining domain.

Limitations. This study only considers single sequence and low resource settings. We have not applied   to the multi-sequence versions of the dataset and neither have we applied it to datasets with more datapoints than n_train = 20. How to leverage pretraining for multi-sequence tasks is a relevant avenue for future work. This study neither tests the SwinUNETR framework using different backbones and nor tests   using a SwinUNETR backbone, since it is not readily compatible with a U-Net decoder.[Since it uses non-pretrained UNETR <cit.> encoder blocks in the interface between the Swin-Transformer and the convolutional decoder.]



§ CONCLUSION

We present , a framework based on Masked Image Modeling to pretrain 3D-native segmentation models which balances memory usage, runtime, and finetuning performance. We further introduce , the largest dataset of brain MRI for pretraining to date. We pretrain   on   and evaluate the models on three challenging datasets. We compare the framework to models trained from scratch as well as the pretrained SwinUNETR model. Our results demonstrate that when  is used to pretrain U-Net models or MedNeXt models, the models outperform similar models trained from scratch, as well as SwinUNETR models, both pretrained and not in the challenging tasks evaluated.
























    





Supplementary Material



§ HYPERPARAMETERS






§ AUGMENTATIONS







§ ADNI ACKNOWLEDGEMENT


Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimag- ing Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not par- ticipate in analysis or writing of this report. A complete listing of ADNI investigators can be found 
http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdfhere.

The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner,MD. The primary goal of ADNI has been to test whether serial magnetic reso- nance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). For up-to-date information, see <www.adni-info.org>.

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart- ment of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & John- son Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transi- tion Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research In- stitute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.




§ CONTRASTIVE AND ROTATION LOSS CURVES






§ RESULTS WITH STANDARD DEVIATION








 Paper Title: Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses.






2024
Copyright for this paper by its authors.
  Use permitted under Creative Commons License Attribution 4.0
  International (CC BY 4.0).



CLiC-it 2024: Tenth Italian Conference on Computational Linguistics, Dec 04 — 06, 2024, Pisa, Italy








1]Gabriele Sarti[
    orcid=0000-0001-8715-2987,
    email=g.sarti@rug.nl,
    url=https://gsarti.com,
]
[1]

1]Tommaso Caselli[
orcid=0000-0003-2936-0256,
email=t.caselli@rug.nl,
]

1]Malvina Nissim[
orcid=0000-0001-5289-0971,
email=m.nissim@rug.nl,
]

1]Arianna Bisazza[
orcid=0000-0003-1270-3048,
email=a.bisazza@rug.nl,
url=https://cs.rug.nl/ bisazza
]


[1]Corresponding author.

[1]Center for Language and Cognition (CLCG), University of Groningen, Oude Kijk in 't Jatstraat 26 
 Groningen, 9712EK, The Netherlands




§ ABSTRACT 
  Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbalized rebuses for the Italian language and use it to assess the rebus-solving capabilities of state-of-the-art large language models. While general-purpose systems such as LLaMA-3 and GPT-4o perform poorly on this task, ad-hoc fine-tuning seems to improve models' performance. However, we find that performance gains from training are largely motivated by memorization. Our results suggest that rebus solving remains a challenging test bed to evaluate large language models' linguistic proficiency and sequential instruction-following skills.






  Large language models Sequential reasoning Puzzle Rebus Crosswords Enigmistica Italiana



Non Verbis, Sed Rebus: 
 Large Language Models are Weak Solvers of Italian Rebuses
    [
    Submitted August 5, 2024
==================================================================================







§ INTRODUCTION


Complex games such as chess and Go have long been a source of inspiration to develop more flexible and robust AI systems <cit.>. Recent developments in NLP suggested that creative language games could be exploited as promising benchmarks for quantifying the ability of large language models (LLMs) to carry out multi-step knowledge-intensive reasoning tasks under pre-specified constraints <cit.>. While crossword puzzles have been historically the main focus of such efforts <cit.>, other categories of linguistic games received only marginal attention, especially for languages other than English. A prominent example of less-studied language games is the rebus, a visual puzzle combining images and graphic signs to encode a hidden phrase. Indeed, rebus solving is a complex, multi-step process requiring factual knowledge, contextual understanding, vocabulary usage, and reasoning within pre-defined constraints – a set of fundamental skills to address a variety of real-world tasks.

In this work, we conduct the first open evaluation of LLMs' rebus-solving capabilities, focusing specifically on the Italian language. We propose a novel strategy to derive text-only verbalized rebuses from transcribed intermediate rebus solutions and use it to produce a large collection with more than 80k verbalized rebuses. We then evaluate the rebus-solving skills of state-of-the-art LLMs, including open-source systems and proprietary models, via few-shot prompting. Moreover, we fine-tune a small but capable LLM on verbalized rebus solving, outperforming state-of-the-art systems by a wide margin. Finally, we conduct a fine-grained assessment of LLMs' sequential reasoning steps, explaining model performance in terms of word complexity and memorization.

Beyond rebus solving, our evaluation sheds light on the limits of current LLMs in multi-step reasoning settings, highlighting challenges with their application to complex sequential instruction-following scenarios.[Code, data and models are available on https://github.com/gsarti/verbalized-rebusGithub and https://huggingface.co/collections/gsarti/verbalized-rebus-clic-it-2024-66ab8f11cb04e68bdf4fb028Huggingface] 









§ BACKGROUND AND RELATED WORK




  
Italian Enigmistica and Rebuses The Italian language is characterized by a rich and long-standing tradition of puzzle games, including rebuses, dating back to the 19th century <cit.>[Refer to <cit.> for a comprehensive overview of peculiarities and norms in modern Italian rebuses.] In Italian rebuses, a first pass (prima lettura) representing an intermediate solution of the puzzle is produced by combining graphemes with underlying image elements in a left-to-right direction (<Ref>). Then, the letters and words of the first pass undergo a re-segmentation (cesura) according to a solution key (chiave di lettura[Referred to as diagramma in jargon.]), which specifies the length of words in the solution (frase risolutiva). The verbalized rebuses we introduce in this work are variants of textual rebuses (rebus descritto or verbis), where the text-based puzzle is crafted by replacing first pass words with their crossword definitions in a templated format (<Ref>).



  
Linguistic Puzzles as NLP Progress Metrics Language games have recently been adopted as challenging tasks for LLM evaluation <cit.>. While works in this area have historically focused on English crosswords <cit.>, recent tests focus on a more diverse set of games such as the New York Times' “Connections” <cit.> and “Wordle” <cit.>. Automatic crossword solvers were also developed for  French <cit.>,  German <cit.> and Italian <cit.>, while didactic crossword generators are available for Italian <cit.> and Turkish <cit.>. Relatedly, the Italian evaluation campaign EVALITA[<https://www.evalita.it>] recently hosted two shared tasks focusing on the word-guessing game “La Ghigliottina” (The Guillotine) <cit.>. To our knowledge, our work is the first to attempt the computational modeling and evaluation of rebus-solving systems. Importantly, language games such as rebuses are not easily translatable into other languages due to their structural and cultural elements. This makes them a scarce but valuable resource for language-specific evaluations of language processing systems.



  
LLMs as Sequential Reasoners State-of-the-art LLMs were shown to struggle to follow sequential instructions presented in a single query <cit.>, but their performances improved significantly with ad-hoc training <cit.>. This acts as an initial motivation for our rebus-solving fine-tuning experiments. In our evaluation, we also adopt few-shot prompting <cit.> and chain-of-thought reasoning <cit.>, which were both shown to strongly improve LLMs' abilities when solving complex multi-step tasks.



§ EXPERIMENTAL SETUP




  
Data We begin by extracting all rebuses' first passes and solutions available on Eureka5[<http://www.eureka5.it>, additional details in <Ref>. Rebus illustrations are not available in Eureka5.], an online repository of Italian puzzles. We refer to the resulting dataset containing 223k unique rebuses sourced from various publications as EurekaRebus. For crossword definitions, we use ItaCW <cit.>, containing 125k unique definition-word pairs. We select only EurekaRebus examples in which all first pass words match an existing ItaCW definition to enable verbalization, maintaining 83,157 examples for our modeling experiments.[Since verbalized rebus are produced from textual contents only, crossword definitions may refer to different word meanings (e.g. [Two soccer players] is used to represent the word “wings” in <Ref> despite not matching the word sense “bird wings” of the original image. This does not affect the validity of our task.] Since several ItaCW words are associated with multiple definitions, we randomly sample definitions to promote diversity in the resulting verbalized rebuses. A test set of 2k examples[Composed by Test id and Test ood, described in <Ref>] is kept aside for evaluation, and the remaining 81k examples are used for model training. 





  
Models We fine-tune Phi-3 Mini 3.8B 4K <cit.>, the most capable LLM below 4B parameters for a wide range of Italian language tasks[<https://hf.co/spaces/FinancialSupport/open_ita_llm_leaderboard>]. We use quantized low-rank adapters (QLoRA; ) for efficient fine-tuning with Unsloth[<https://github.com/unslothai/unsloth>] and Transformers <cit.>, training the model for 5,000 steps with a batch size of 16 over 81k examples. For comparing our model performances, we select GPT-4o <cit.> and Claude-3.5 Sonnet <cit.> as the current state-of-the-art for proprietary LLMs and the instruction-tuned variants of Qwen-2 72B <cit.> and LLaMA-3 70B <cit.> 
as the best-performing open-source LLMs according to the Invalsi Italian benchmark <cit.>. These four systems are used as untrained baselines thanks to their instruction-following abilities and prompted for rebus solving in a few-shot setting. 



  
Format <Ref> presents an example in the templated format used for fine-tuning Phi-3.[An English example is available in <Ref>] The model is prompted to reason step-by-step by 1) solving crossword definitions sequentially (definition resolution); 2) producing a first pass copying letters and definitions' words; 3) re-segmenting it into solution words based on the solution key (solution segmentation); and finally 4) producing the solution by copying re-segmented words. We automatically convert rebuses in this format by deriving the solution key from solution word lengths and dynamically infilling the available information into the template. We use a similar format for prompting experiments, with five in-context step-by-step demonstrations and an explicit instruction asking the model to stick to the previous examples' format to streamline solution parsing.



  
Metrics For our granular evaluation of rebus-solving performance, we adopt the following set of metrics focusing on the first passes (FP) and solutions (S) generated by LLMs:


    
  * Definition (Def.): Proportion of correctly guessed words during definition resolution.
    
  * First Pass Words/Letter Accuracy: Proportion of correct words and letters in the generated first pass. Lower scores may indicate issues with assembling a first pass from previous information.
    
  * First Pass Exact Match (EM): Proportion of generated first passes matching the gold reference.
    
  * Solution Key Match: Proportion of generated solution words matching the lengths specified by the solution key. Lower scores may indicate difficulty in respecting the given length constraints.
    
  * Solution First Pass Match: Proportion of first pass characters employed to construct solution words. Lower scores indicate issues with using generated first pass characters in the solution.[In practice, we define this as 1 - CER(FP, S), where CER is the character error rate <cit.> between the two sequences (lowercased, whitespace removed) computed with https://github.com/jitsi/jiwerJiwer]
    
  * Solution Words Accuracy: Proportion of correct words in the generated solution.
    
  * Solution Exact Match (EM): Proportion of generated solutions matching the gold reference.




§ RESULTS


<Ref> presents our evaluation results. We observe that all prompted models perform poorly on the task, with the overall best prompted system (Claude 3.5 Sonnet) obtaining the correct solution only for 24% of the 2k tested examples. Notably, open-source systems perform significantly worse than proprietary ones, producing correct first passes only for 4% of the examples, and next to no correct solutions. Our fine-tuned system largely outperforms all state-of-the-art prompted models, predicting the correct solution in 51% of cases. From first pass metrics, it is evident these results can be largely explained by the poor word-guessing capabilities of the models, which are greatly improved with fine-tuning. For prompted models, the slight decrease in scores between Def. and FP Words also highlights issues with copying predicted words in the expected format. Finally, we observe that fine-tuning strongly improves the constraint-following abilities of our system, with prompted systems being less strict with applying length and letter-choice constraints for their solutions (Key/FP Match).





§ WHAT MOTIVATES MODEL PERFORMANCES?


In light of the strong performances achieved by our relatively small fine-tuned system, this section conducts an in-depth investigation to identify factors motivating such performance improvements.



  
Word Complexity and Frequency Affects LLM Fine-tuning Performance
 For every word in the first passes and solutions of test set examples, we measure LLMs' overall accuracy in predicting it for the full test set. We then correlate this score to various quantities that could motivate LLMs' performances. More specifically, we use 1) the word frequency in the training set; 2) the word frequency in Paisà <cit.>, a large web Italian corpus; and 3) the length of the word (number of characters). We find a significant positive correlation (ρ = 0.44) between first pass word prediction accuracy and training frequency for the fine-tuned Phi-3 model, suggesting that model performance is strongly related to training coverage. The length of characters is also found to negatively affect our model's performance, albeit to a smaller extent (ρ = -0.11). The performance of prompted models is unrelated to both properties for first pass words, indicating that these results are the product of fine-tuning.[Paisà frequency is never found to correlate significantly. Full correlation results are available in <Ref>.]



  
LLM Fine-Tuning Fails to Generalize to Unseen Words






To further confirm the importance of fine-tuning word coverage in defining model performances, we evaluate our fine-tuned model in out-of-distribution settings. For this evaluation, the 2k examples of the test set from previous sections are divided into two subsets: one in which all first pass words were seen during fine-tuning by Phi-3 (Test id, 1061 examples) and one in which, for every example, at least one first pass word was unseen in training (Test ood, 939 examples). Intuitively, if Phi-3 performance is mainly motivated by memorizing fine-tuning data, introducing OOD words should produce a significant drop in model performances. Results shown in <Ref> confirm that this is indeed the case. We find Phi-3 performances to be near-perfect on seen first pass words (FP W._ID = 0.96) in both test sets, with a major drop for OOD words (FP W._OOD = 0.20). This produces second-order effects on subsequent steps, causing the FP EM results to drop by 71% (FP EM Test Δ), while significantly impacting downstream solution accuracies. On the contrary, GPT-4o few-shot prompting performances remain nearly identical on both splits, confirming that these results are not the product of a skewed data selection process. Overall, these results strongly suggest that memorization is the main factor behind the strong rebus-solving performance of our fine-tuned LLM.



  
Manual Inspection

We conclude by manually evaluating some generations produced by the best-performing LLMs. <Ref> presents two examples with definitions (D) and solution (S) words predicted by three LLMs, with more examples provided in <Ref>. We use naw as short-hand for “Not A Word” to mark nonsensical terms.

In the first example, Phi-3 correctly predicts all first pass and solution words. On the contrary, other models make several mistakes in the first pass, leading to incorrect solutions. Both prompted models tend to ignore first pass words when these cannot be assembled to form sensical, length-fitting solution words. For example, for D1 GPT-4o predicts p (naw), which would lead to the solution word “SAPpTE” (naw), but the S8 = “Spettacolo” (show) is predicted instead by the model). In particular, GPT-4o appears to prioritize grammatically correct solutions at the cost of ignoring first pass words and solution key length constraints, while Claude 3.5S shows an improved ability to follow these constraints, as confirmed by Key/FP Match results of <Ref>.

In the second example, the first pass word D2 = salice (willow) is OOD for Phi-3. Consequently, the model produces the incorrect prediction aro (naw), and the error is propagated to all solution words, as previously observed in the Test OOD column of <Ref>. Prompted models also underperform in this example, with errors on D1 and D2 propagating to most solution words. However, we note that D1 and D2 incorrect predictions for Claude 3.5S satisfy the provided definitions, suggesting that access to more explicit information about the given constraints could further boost LLMs' performance on this task.



§ DISCUSSION AND CONCLUSION


This work introduced a verbalized rebus-solving task and dataset for evaluating LLMs' sequential instruction following skills for the Italian language. We crafted a large collection of 83k verbalized rebuses by combining rebus transcriptions with crossword definitions and used it to evaluate the rebus-solving skills of state-of-the-art LLMs. Our experiments revealed the challenging nature of this task, with even the most capable prompted models achieving only 24% accuracy on solutions.

While fine-tuning a smaller LLM dramatically improved performance to 51% solution accuracy, our analysis uncovered that these gains were largely driven by memorization and do not generalize to out-of-distribution examples. These results suggest important limitations in the generalization capabilities of current systems for sequential instruction following tasks. Our manual analysis further shows that LLMs seldom account for length constraints when solving definitions, despite the fundamental role of these cues in restricting the pool of possible words. These results suggest that search-based approaches accounting for constraints more explicitly might improve puzzle structure adherence, as previously shown by <cit.>. Other augmentation techniques employing LLM reformulation skills can also be explored to mitigate overfitting.

Future work in this area should focus on expanding similar evaluations to a wider set of languages, input modalities, and puzzle categories, creating a comprehensive benchmark to test LLMs' puzzle-solving skills. Importantly, the task of solving visual rebuses and their more convoluted variants[For example, rebuses requiring first pass anagrams (anarebus) or dynamic relations derived from multi-scene analysis  (stereorebus)] remains far beyond the current capabilities of vision-language models. Hence, solving these puzzles automatically can be considered an important milestone in developing multimodal AI systems for constrained multi-step reasoning tasks. Our results confirm that the challenging nature of rebuses, even in their verbalized form, makes this task valuable for assessing future progress in LLMs' linguistic proficiency and sequential reasoning abilities. Finally, our rebus-solving LLM can facilitate future interpretability work investigating the mechanisms behind factual recall and multi-step reasoning in transformer models <cit.>.



  
Limitations 
Our analysis was limited to a relatively small set of models, and a single prompt template obtained after minimal tuning. Further experiments are needed to verify that memorization patterns after fine-tuning remain relevant for other model sizes, prompt formats, and training regimes, particularly for full-weight training approaches.


   Gabriele Sarti and Arianna Bisazza acknowledge the support of the Dutch Research Council (NWO) for the project InDeep (NWA.1292.19.399). 
   Arianna Bisazza is further supported by the NWO Talent Programme (VI.Vidi.221C.009).
   We are grateful to the http://www.enignet.it/homeAssociazione Culturale “Biblioteca Enigmistica Italiana - G. Panini” for making its rebus collection freely accessible on the Eureka5 platform, and to Valeriya Zelenkova for her valuable comments on the first version of this work.








§ ADDITIONAL DATA INFORMATION





  
Dataset statistics <Ref> presents statistics for the EurekaRebus dataset and the filtered subset we use for composing verbalized rebuses. The ItaCW dataset contains a total of 125,202 definitions for 40,963 unique words, with the most frequent words having hundreds of different definitions, e.g. 173 for re (king), 155 for te (you). Definitions used for verbalization are randomly sampled from the pool of available definitions for every word.







  
First pass/Solution word distribution <Ref> shows the distribution of first pass and solution words for the filtered EurekaRebus subset used in our work.



§ ADDITIONAL EXPERIMENTAL RESULTS



<Ref> presents the correlations between model accuracy and the properties presented in <Ref>. <Ref> presents the full ID/OOD performances for all tested models, showing consistent results with <Ref> for all prompted models. <Ref> presents Phi-3 Mini performances across rebus-solving fine-tuning steps.













§ ADDITIONAL MODEL GENERATIONS



<Ref> presents an English translation of <Ref> example using the prompt format adopted in this study. <Ref> provide additional example of LLM generations for tested rebuses, with the example from <Ref> (bottom) being OOD due to the manovella (crank) word in D2, and the others being ID for the fine-tuned Phi-3 Mini.









 Paper Title: Rolling in the deep of cognitive and AI biases.


printacmref=false



















avakali@csd.auth.gr

  Aristotle University
  Thessaloniki
 
  Greece



tantalaki@csd.auth.gr

  Aristotle University
  Thessaloniki
  Greece




 























§ ABSTRACT 
Nowadays, we delegate many of our decisions to Artificial Intelligence (AI) that acts either in solo or as a human companion in decisions made to support several sensitive domains, like healthcare, financial services and law enforcement. AI systems, even carefully designed to be fair, are heavily criticized for delivering misjudged and discriminated outcomes against individuals and groups. Numerous work on AI algorithmic fairness is devoted on Machine Learning pipelines which address biases and quantify fairness under a pure computational view. However, the continuous unfair and unjust AI outcomes, indicate that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. Although, the synergy of humans and machines seems imperative to make AI work, the significant impact of human and societal factors on AI bias is currently overlooked. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. Inspired by the cognitive science definition and taxonomy of human heuristics, we identify how harmful human actions influence the overall AI lifecycle, and reveal human to AI biases hidden pathways. We introduce a new mapping, which justifies the human heuristics to AI biases reflections and we detect relevant fairness intensities and inter-dependencies. We envision that this approach will contribute in revisiting AI fairness under deeper human-centric case studies, revealing hidden biases cause and effects.  







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[500]AI
[300]Philosophical/Theoretical foundations of AI
Cognitive science
[100]













Rolling in the deep of cognitive and AI biases
    Nicoleta Tantalaki
    August 5, 2024
==============================================






§ INTRODUCTION
 
AI technology has invaded everyday life and activities, disrupting societal, economical, and policy making norms. AI acts either in solo or as a human companion in decisions made in so many domains: to provide health insights, approve loans, detect potential criminals and terrorists, screen University admissions and job applications, allocate digital content and so on <cit.>. The AI systems' ability to acquire cognitive skills such as perception, reasoning, and decision-making makes AI a valuable technology <cit.>. However, the hypothesis that AI's mathematical and statistical nature will be more  neutral, objective, and rational than humans, is not evident yet! In so many real-world cases there are several problems, associated with AI algorithms, based on the fact that they are often proven to be biased <cit.>. 



Up to now, bias and fairness terms are perplexed, studied under varying hypotheses and addressed with ad-hoc definitions and divergent goals. For example, in statistics, bias is a systematic error in the estimation of parameters or variables <cit.>. In this context, the effectiveness of AI systems was based on this definition and assessed based on how well their outputs correspond to their inputs. Thus, AI to be fair, meant that it had to deliver high accuracy based on the training data chosen and effectively generalize to unseen data. Apparently, the full spectrum of risks posed by bias in AI cannot be encompassed sufficiently by such a statistical perspective <cit.>. AI as a human mirroring technology, reflects both the fair and unfair patterns in society, and the perspectives of its creators, whether these perspectives are fair or biased <cit.>. 

In pursuit of Fairness in Artificial Intelligence (FairAI), a lot of research efforts explored the importance of data in mitigating bias and creating equitable outcomes. Conceptualizing "fairness" necessitated examining various data characteristics, like demographic information, from a technical perspective <cit.>. Computational methods, though, like data re-sampling or re-weighting prove to be not enough, are costly, and demand prerequisites that are not feasible in real-world datasets <cit.>. Rich and emerging bibliography propose solutions that are, though, generally grounded on computational AI pipelines, presume specific bias types over the phases of data collection (pre-processing), modeling (in-processing), and deployment (post-processing) phases, propose technical methodologies as bias mitigation strategies, and choose among numerous fairness metrics of questionable efficiency that are challenged by decision makers' own bias <cit.>. Although such studies have always been crucial for bias mitigation, they cannot guarantee a bias-free model <cit.>. Unfortunately, even when a system is carefully designed to be fair, AI biases remain and harm minority or unprivileged populations (based on gender; age; vulnerable; and other traits), making the need to understand AI systems as sociotechnical systems urgent. 

Recently, due to the increasing deployment of AI applications in sensitive domains like health-care and law enforcement, practitioners and scholars underlined the complexity of managing AI bias and the necessity to include behavioral and social aspects in their examination <cit.>.  The collaboration of both humans and AI is essential to make this technology work as intended but the significant impact of human factors on AI bias is currently overlooked <cit.>.
Human-AI synergy settings, must carefully consider that AI decisions and outputs are influenced by human systematic errors in thinking, which are known as cognitive biases. Cognitive biases relate to the outcome of utilising heuristics while thinking <cit.>, are common to all human beings, and influenced by their own reality, that is based on cognitive limitations, motivational factors, and/or adaptation to natural environments <cit.>. Given the strong human tendency to many bias types that often operate unconsciously <cit.>, it has been noticed that human biases are not easily formulated and promptly recognized. Thus, they often “creep” into each phase of the AI lifecycle with unpredictable and complicated ways <cit.>. According to the literature, more than 180 cognitive biases have been identified over the last six decades of research on human judgment and decision-making in cognitive science and this list is continually evolving <cit.>. 

In this work, we explore the knowledge about bias and fairness, coming from the mature area of research in cognitive science, and we argue that to introduce an actual human-centric FairAI demands a deep study of human and AI biases traits, patterns, and dynamics. We aim to provide a stepping stone in revealing the cognitive heuristics that underlie many identified biases in AI. To do so, we follow a systematic methodology, which : (a) sets human and AI bias correlation assumptions and proceeds with deep hypothesis testing ; (b) uncovers the particular heuristics which lead to specific harmful actions at each phase of the AI lifecycle (pre-processing, in-processing, post-processing) <cit.>; (c) proposes a systematic mapping under an explorative analysis among 
human heuristics, harmful actions, and specific distorted and harmful biased outcomes. In such a systematic manner, specific AI biases are recognized as reflections of these heuristics and hidden intensities and inter-dependencies among them are revealed. Our work relies on the scientifically grounded heuristics, introduced and mentioned in Daniel Kahneman's prize-awarded research with other researchers <cit.>. The AI biases (or computational biases) discussed in this study are some of the most relevant in the context of AI and the most studied in literature <cit.>. 
Overall, we aim to offer new insights and a new theoretical framework which will roll in the deep of biases and fairness origins and dynamics, towards introducing a new line of human-centric FairAI research. At such a research line, we will  bridge the knowledge and the evidence coming from cognitive and AI sciences, recognizing that it's not possible to either quantify human biases or to humanize AI biases. Thus, we propose a solid methodology for systematically mapping the human and AI biases, in support to a novel FairAI research practice which can no longer be purely computational, and which must be adaptive and human-inclusive in an end-to-end manner.  



§ HUMAN HEURISTICS AND BIASES BEHIND DECISION-MAKING

A recent NIST special publication about standards for bias and fairness, has positioned computational biases at the tip of a biases iceberg, emphasizing current research inefficiency to deeper study human and systemic (real world) biases <cit.>. It’s now urgent to go deep in the bias iceberg, taking advantage of the long studied topics and the scientific evidence revealed by cognitive science   about human cognition and biased opinion making. Identifying causes of cognitive biases, though, is too challenging. In the groundbreaking work of Tversky and Kahneman "Judgment under Uncertainty" back in 1974 researchers argued that a broad family of cognitive biases can be explainable in terms of three heuristics; representativeness, availability, and anchoring and adjustment <cit.> (also addressed in their collection of papers <cit.>). Later in 2002, in "Heuristics and Biases: The Psychology if Intuitive Judgement" <cit.>, Thomas Gilovich, Dale Griffin, and Daniel Kahneman have pulled together a new collection of papers to revisit each of these three major general purpose heuristics, reinforcing their importance. The Nobel Prize in Economic Science awarded to Kahneman in 2002 is certainly one indicator of the tremendous impact of heuristics and biases research. Another major theme underlined in this collection (<cit.>) is the fact that intuition should also be explained considering the role of affect, mood, and emotion. The affect heuristic was introduced by Paul Slovic et al. <cit.> and later mentioned in more Kahneman's works <cit.>. These heuristics are briefly summarized next, to inspire our new FairAI hupothesis testing base. 
Representativeness is about estimating the probability of an event by comparing it to a known situation or a prototype that already exists in someone's mind. People overweight certain information because they believe it's representative of reality. This may happen because this piece of information is more recent, or has been emphasized more in the media, or has been presented in a certain way. It’s a mental shortcut that helps people group the world into simple categories like X and Y, but when a new experience or data rises, they cannot consider that it could be an entirely new category Z. The representativeness heuristic leads to the bias that makes people believe that a stereotype is true. A prominenet example of bias introduced by representativeness is the illusion of validity that refers to the overconfidence that is produced when we have a good fit between the predicted outcome and the input information <cit.>. 
Availability is a type of mental shortcut that involves assessing the frequency or plausibility of something based on how easily examples are brought to mind, without making an effort to look for other, potentially more significant information. Instances of large classes are usually recalled better and faster than instances of less frequent classes, likely occurrences are easier to imagine than unlikely ones, and the associative connections between events are strengthened when the events usually co-occur. People use this heuristic for estimating the number of elements in a class, the likelihood of an event, or the frequency of co-occurrences, by how easily the relevant mental operations of retrieval, construction, or association can be performed. A prominent bias that belongs in this category is the illusory correlation that makes people conclude that variables are correlated because their pairings come to mind easily (e.g. because they are quick to grasp, or because they seem likely) <cit.>.
Anchoring and adjustment occurs when people base their initial ideas, estimates or predictions on a piece of information and make changes driven by this starting point. The anchoring bias refers exactly to this tendency of people to rely heavily on a reference point that is called "anchor" (and can be completely irrelevant e.g. an arbitrary number) and subsequently adjust that information until a final decision. Often, these adjustments are inadequate and remain close to the original anchor <cit.>. Automation bias is a well-known bias in the context of AI that belongs in this category. It occurs when individuals rely heavily on automated systems or technology to make a decision <cit.>. Individuals may sometimes gravitate toward confirming their initial views which are the anchor, making insufficient adjustments, selectively gathering information that reinforces their starting point, giving rise to a well studied cognitive bias, known as confirmation bias <cit.>. This type of bias is capable of involving both information processing and emotions as discussed below <cit.>. It should also be noted that overconfidence's cognitive causes are among others the anchoring and the confirmation bias, as people anchor in a value or an idea without making the necessary adjustments. Instead, a competent strategic thinker would look for disconfirming evidence and alternative explanations for those signals that are ignored by others <cit.>.

Affect involves making decisions based on emotions and feelings associated with the given options. Decision makers’ fast reactions to decision options, rather than analytical reasoning, serve as input to decisions. The affect heuristic is a key example of Kahneman's fast and intuitive i.e. fast so called "System 1 thinking" <cit.>, where people jump to a conclusion and bypass the process of gathering information, giving rise to a type of bias called conclusion bias or prejudgement. However, people may also engage in deliberate thinking (mobilizing the so called "System 2 thinking", i.e. the one that is slow, conscious and requires intentional effort <cit.>), and their emotional comfort of having consistent beliefs, drive their tendency to seek arguments and favor information that confirm preexisting beliefs, giving once again rise to confirmation bias. <cit.>.


The anchoring bias is a subtler example of conclusion bias that makes people start the process of judgment with an inclination and jump to a conclusion just by consulting their feelings  refers to people's tension to look for patterns, find them quickly (even if they’re not there) and then get wired to look for coherence<cit.>. 
Excessive coherence comes up when someone reads too much into a situation, finding causes and relationships where they do not exist.The most coherent stories, though, are not necessarily the most probable. They are plausible, and the notions of coherence, plausibility, and probability can be easily confused by the human mind. 
As an example in this category, we could think of someone's overconfidence, that is, belief in the story <cit.>. A competent strategic thinker would look for disconfirming evidence and alternative explanations for signals that are ignored by others. 
The Dunning-Kruger effect is a known cognitive bias, in which people wrongly overestimate their knowledge or ability in a specific area due to lack of self-awareness and inability to accurately assess their skills. Confirmation bias is also a result of excessive coherence, and refers to the tendency to collect and favor information that confirms one’s preexisting beliefs<cit.>.





§ COMPUTATIONAL BIAS TYPES IN THE AI LIFECYCLE 

Cognitive biases (we also refer to them as "human biases") described above, may enter the engineering and modeling processes and are ubiquitous in the decision making processes across the whole AI lifecycle, and in the use of AI applications once deployed <cit.>. In the context of AI, the typical approach of employing FairAI research and practices involves exploring specific bias types (known as "computational bias") throughout the AI lifecycle <cit.>. The following seven computational bias types are distinguished as the most prominent in the context of AI and the most studied in literature:


  * Historical bias results from practices that lead to a specific treatment towards certain social groups being advantaged or favored, while others may be disadvantaged or excluded. Biases in this category arise even if data is flawlessly sampled and can be present in the datasets used prior to the creation of the model. They are the result of the majority following existing rules or norms. Racial biases, gender biases, biases towards people with disabilities belong in this category  <cit.>.

  * Representation bias occurs when building datasets to sample from a population. A non-representative dataset lacks the diversity of the population. The sample might be small, wrongly sampled, with a distribution that differs from the true underlying distribution of the relevant population, and neglect underrepresented groups. Even with perfect a sampling technique, a model can have bad performance for the underrepresented groups dew to possible skewness of the underlying distribution. A model trained with fewer data points regarding a subgroup will not generalize well for it <cit.>. Sampling bias mentioned in statistics belongs in this category and arises when the chosen sample represents a skewed subset of the target population <cit.>. 

  * Measurement bias arises from the choices made when choosing and computing particular features and labels. Usually, poor reflections are used as proxies of complex constructs. The method and accuracy of measurement may also vary across groups, found in the relevant population <cit.>. As an example, using historical school performance as a proxy to predict the abilities of students, who could not take proper exams during COVID-19, introduced measurement bias that unfairly affected them. 

  * Aggregation bias arises when false conclusions are drawn about individuals from observing the entire population. Such biases come up even when different subgroups are represented equally in the training data but are inappropriately combined. Any general assumptions about subgroups should be avoided <cit.>. For example, models for diagnosing and monitoring diabetes have relied on Hemoglobin A1c (HbA1c) levels for predictions. However, a recent study revealed that HbA1c levels vary in complex ways across different ethnicities <cit.>, making it likely that a single model for all populations would display aggregation bias. 

  * Algorithmic bias occurs due to inappropriate technical considerations taken while building a model and have consequences related to fairness. Such choices regard aspects ranging from dataset split to different sets (e.g. training set, test set) to model's architecture and choices regarding optimization functions and hyper-parameters. Several mathematical abstractions are needed to develop a model and are made without prejudice or discriminatory intent <cit.>. However, they disproportionately impact error rates on the underrepresented groups of the dataset. The key reason that these modeling choices lead to bias has to do with the fact that fairness often coincide with how underrepresented and protected features are treated by the model <cit.>.

  * Evaluation bias occurs when the benchmark population that is used during model evaluation, is not representative of the target population and/or inappropriate performance metrics are used <cit.>. Let's consider a smile detector model trained on a dataset lacking adequate representation of Asian individuals. If the testing benchmark is similarly unbalanced, mirroring the training set, the bias against Asians will go unnoticed <cit.>

  * Deployment bias arises when the problem the model is designed to solve is different from the way it is actually utilized. This often occurs when a system is built and evaluated as if it were fully autonomous as human reviewers may overestimate its credibility leading to automation bias <cit.>. 





§ SYSTEMATIC MAPPING BETWEEN HUMAN HEURISTICS AND COMPUTATIONAL BIASES


In our methodology, we explore specific human actions as sources of biases which are then spotted at the pre-processing, in-processing, and post-processing phases of the AI lifecycle <cit.>. The selected harmful actions resulted from our exploratory analysis of the most popular computational biases (Section 2) commonly highlighted in several recent surveys <cit.>. In tandem with the Information Commissioner's Office (ICO) Annex regarding "Fairness in the AI lifecycle" <cit.>, that sets out potential sources of bias, we harvest the necessary information around the decisions that may lead to unfair outcomes in the context of AI. Building on the grounded evidence that humans may take wrong or sub-optimal decisions, leading to problematic actions, based on the necessary but (often) biased four heuristics, we view computational biases as reflections of these human heuristics (Section 2). Then the explored human, i.e. cognitive biases are reflected to the prominent computational biases (Section 3), and an insightful mapping among them is provided. Our approach offers a solid scientific evidence coming from our rolling in the deep interdisciplinary study, which bridges cognitive and AI scientific knowledge, and reveals hidden human and AI ties which have been largely overlooked. 




 §.§ Pre-processing

In any typical AI pipeline, data must first be collected and a target population is identified, along with a set of features and labels chosen. Surely, the large scale of data, prohibits the inclusion of the entire data population, thus certain groups may be excluded or underrepresented, leading to inaccurate results that are harmful when making important decisions. 

Harmful action 1: Use of a non-appropriate (and non-representative) sample

This action leads to results, applicable to a subset of the relevant population and not to the broader population itself. The chosen sample may seem to be representative of the relevant population, while it is not. The variability within the population may have not been taken into consideration, the size of the sample used may not be the appropriate or the method of selecting the sample may be uneven or limited <cit.>.
Heuristics mapping: From a cognitive perspective, the representativeness and availability heuristic impact AI lifecycle at such a sampling step. For instance, it has been noted that during training, most AI   face recognition algorithms, make use of pictures from people from developed countries, as people assume that these pictures are representative of the world population <cit.>. People consistently overestimate the proportion of the world that is similar to themselves. This is why so much technology is optimized for able-bodied, white, cis-gender, males, because this population makes up the majority of developers <cit.>. Darker skin tones are underrepresented or even excluded, yielding less accurate results for dark skinned people. Moreover, already available or accessible datasets are usually preferred instead of the most suitable dataset. The availability heuristic rises due to the natural human tendency to place greater emphasis on the most readily available data, even if it does not fully represent the entire population <cit.>. The streetlight effect is a type of cognitive bias that arises when people only search for something where it is most convenient to look. As an example, researchers may assume that easily available, user-generated data from the Internet depicts reality, and is enough but this is not true.Large and easily available datasets do not always depict the world we live in.
Reflections: The resulting AI bias is the representation bias resulting in a model that fails to generalize well for certain subgroups of the relevant population. It is worth to mention that even given a perfect sampling, we can also face historical biases as datasets used may be available, and seem representative, but exhibit entrenched biases. Baked-in biases in the overall population (as results of people's bigotry) may regard disadvantaged groups including indigenous populations, women or disabled people and are perpetuated to the whole AI lifecycle, prompting discrimination<cit.>. As an example, CrimSAFE's algorithm was developed as a tenant screening algorithm to review and score potential renters in the United States. This algorithm disproportionately and illegally screened out Black and Latino applicants based on their criminal records. However, Black and Brown Americans are much more likely to be racially profiled, stopped and targeted by police for arrest <cit.>. This dataset reflected the already baked-in historical (also known as societal) biases, leading to a system that reinforced patterns of housing discrimination.


Models trained on datasets with a skewed distribution (with a small number of well-represented features and several features that are relatively underrepresented) yield error rates on the underrepresented groups and possible protected attributes and prompt discrimination.

Harmful action 2: Use of an already biased sample. 

Training data include systemic biases when they are gathered from contexts characterised by inequality and result in discriminatory AI systems <cit.>.

Heuristics mapping: This bias is not a problem related only to AI developers, but is related to all humans. Already existing bias in the world can creep into the selected sample, even given a perfect sampling. The representativeness heuristic may be behind a lot of people's bigotry as this heuristic make people believe that a stereotype is true and this can be reflected by the selected dataset <cit.>.

Reflections: We face systemic biases as datasets may be available and seem representative but still exhibit entrenched biases. The baked-in systemic biases that come up at this stage, may regard disadvantaged groups including indigenous populations, women or disabled people that are consistently underrepresented <cit.>. As an example, CrimSAFE's algorithm was developed as a tenant screening algorithm to review and score potential renters in the United States. This algorithm disproportionately and illegally screened out Black and Latino applicants based on their criminal records. However, Black and Brown Americans are much more likely to be racially profiled, stopped and targeted by police for arrest <cit.>. This dataset reflects systemic bias, leading to a system that reinforces patterns of housing discrimination.



Harmful action 2: Misuse of proxy variables.

Usually a proxy, that is a concrete measurement, is chosen to approximate a construct i.e a concept, that is an unobservable or immeasurable variable. These proxy variables selected for a model can be poor reflections of target contrasts and/or be generated differently across groups. As an illustrative example, temperature may be used as a potential proxy for the “beauty” of the weather. This proxy is a poor reflection of the target construct. Ice cream sales are not dependent on the temperature but on sunshine <cit.>. In order for a variable to be a good proxy, it must have a close correlation with the variable of interest.
Heuristics mapping: A person who chooses which variables to include or avoid may decide in a way that aligns with their beliefs and impart their own cognitive biases into the model. The representativeness heuristic make people believe that a proxy is a good reflection of the true variable and is therefore used <cit.>. A researcher may also use different measurement process across different groups due to confirmation bias. As an example, a fraud analyst might examine thoroughly some groups over others but these higher rates of testing will yield more positives and thus confirm the analyst's initial beliefs and prejudgements, skewing the observed base rates <cit.>. The availability heuristic can also lead to the use of a proxy that seems to relate to the inferred cause but such apparent correlation might be due to a third, hidden variable that is not available <cit.>. Illusory correlation (identified in Section 2 as an availability bias) makes an individual's perception of the relationship between two variables distorted, creating a false connection <cit.>. 
Reflections: Measurement bias occurs at this phase. Researchers may choose imperfect proxies for the true underlying values or include protected attributes and these actions result in inaccurate or/and discriminant classifiers <cit.>. Especially when it comes to protected attributes as proxy variables, unintended information about people can be revealed. Protected attributes partition a population into different groups that should, though, be treated equally. Developers avoid utilising protected attributes associated with social groups which have historically been discriminated against, as features in the training set. This would lead to a model that relies on sensitive information, yielding biased outcomes (direct discrimination). Removing the sensitive feature, though, does not prevent discrimination, as latent variables can be inferred implicitly e.g.gender can be inferred through browsing history, and race can be inferred through zip code <cit.>. Indirect discrimination, refers to cases where groups or individuals are treated less favorably based on rules that disadvantage a protected group, while they seemed to be neutral. Redlining was a practice in the US, used to deny financial services in people based on their postal code. Neighborhoods that were supposed to be "risky" were outlined, but although postal code might seemed a neutral feature, it was highly correlated with ethnicity, leading to discrimination against African-Americans <cit.>.


 §.§ In-processing
 This phase involves the model design, development, and evaluation. A model is trained to optimize a specified objective (e.g. minimize a loss function) and its quality is often measured on benchmarks i.e available datasets (different from the training data). What is needed is a model able to generalize on new, unseen data. The recent deployment of AI applications in social sensitive domains put forth a range of other important desiderata apart from accuracy, that models should be aiming for like fairness and privacy. Researchers use different benchmarks to estimate model's generalizability and select appropriate metrics and criteria that reflect the goals that had been initially set.

Harmful action 1: Make inappropriate model design choices.
 
Humans' model design choices regard aspects that range from the model’s objective function to hyperparameter settings. What has to be understood is which model design choices disproportianately amplify error rates on features that may be underrepresented and possibly protected, raising fairness issues <cit.>.
Heuristics mapping: Decisions and who makes them at this phase can be driven by the representation, availability and affect heuristic. Information may be flattened as analysts seek patterns and try to come up with easier problems to solve even without being aware of this substitution (guided by representativeness and availability heuristics <cit.>. Certain objectives may rise as more important than others based on someone's existing beliefs and stereotypes that fill gaps in information. Consequently, choices give rise to harmful bias. Such choices are usually made in the absence of discriminatory intent. Simple models with fewer (and already available) parameters may be preferred, as they tend to be less expensive to build, more explainable, and transparent. By approximating a real-life problem, which is complicated, by a much simpler model, makes outputs easier to understand and this might be tempting, but introduces bias <cit.>. The choice of models' desiderata and hyperparameters is another matter of crucial importance at this phase. An analyst may believe that it is imperative to optimize for privacy guarantees in a health-care system based on their prior experience and inner beliefs, emphasizing the emotional importance of protecting personal information, but this can come at the cost of accuracy as discussed below <cit.>. 
Reflections: Several “flattening” techniques occur during inevitable mathematical abstractions leading to algorithmic bias. First of all, the choice of the model’s objective function can lead to biased results for several sub-populations. Simple models fail to capture regularities in the data and tend to underfit the data. A model that does not generalize well for underrepresented groups causes harm and leads to discrimination for these underrepresented groups. In comparison, a model with high variance (and low statistical bias) may represent the data set accurately but could lead to overfitting to noisy or unrepresentative training data <cit.>.
Moreover, when it comes to the choice of a model's desiderata, there are several known trade-offs that have to be considered <cit.>. As an example, the loss function is used to evaluate how well the model fits the data. Loss on the training data has to be typically minimized, however, if the loss function is biased towards a specific group (e.g., white patients in a population), the relevant model will be better trained for this group. In this way, maximizing accuracy do not hold static other properties that are important like fairness<cit.>. As another example, differential privacy, which is the current state of the art in private machine learning, uses approaches like gradient clipping and noise injection. The cost of differential privacy, though, is a reduction in the model’s accuracy and this can affect disproportionately underrepresented groups and model's fairness<cit.>. Furthermore, error rates on features that are relatively underrepresented can get impacted disproportionately by even more subtle choices. Early stopping is a technique used to reduce overfitting without compromising model accuracy. However, underrepresented features are learnt later in the training process <cit.>. Model design choices should be made wisely to achieve an ideal balance between multiple model desiderata and avoid bias, as such choices definately express a preference for final model behavior.

Harmful action 2: Use of non-appropriate (and non-representative) benchmarks and inappropriate performance metrics. 

During model evaluation, model's performance is measured on new and unseen data, usually called the test set. The main problem once again arises from a non-representative dataset that is now used to evaluate the model. The test data has to be different from the data used for training as the same dataset would lead to overoptimistic and overfitted models to a particular benchmark. The benchmark itself has to be representative of the population that the model will serve to avoid models that perform well only on a subset of the relevant population. Evaluation metrics are used to assess the performance and the effectiveness of a model.
Heuristics mapping:  Usually, the model developer has to split the whole dataset into training and test data. Thus, the test dataset used may be once again non-representative of the population due to representativeness and availability heuristics as discussed in subsection 4.1. Empirical studies have also reported confirmation bias in software testers as a result of their tendency to design tests that confirm, rather than question the correct function of an algorithm. Testers may anchor on the belief that the algorithm is accurate and focus on scenarios or inputs that are consistent with this initial assumption, rather than exploring a broader range of possibilities that could challenge their initial belief <cit.>. 
Reflections: The resulting bias at this phase of the AI lifecycle is the evaluation bias. Choosing the wrong benchmark data can lead to overlook and perpetuate potential biases.As an example, a named entity recognition (NER) algorithm may be trained on a dataset with more female names as opposed to male names being tagged as non-person entities or not being tagged at all. In case a benchmark dataset primarily composed of male names is used, the NER algorithm will show good performance on it but experience disproportionately high error rates with female names that won't be even recognised as person enitities. However, in case a benchmark with more balanced gender representations is used, the misclassification of female names will be revealed. 
Several metrics are used during a model's evaluation. Traditional model evaluation relies on common metrics like accuracy and precision to assess a model's performance. These metrics don't really explain if the model is fair to different groups of people, leading to unfair treatment that can be based on things like race, gender, or age.  A subgroup validity approach is needed in this case, and protected attributes come now at handy to let analysts know the necessary group information (whether an individual belongs in a protected or unprotected group). Performance metrics are used across groups to avoid the use of one aggregated metric. Several fairness metrics are proposed in the literature but the plethora of fairness metric definitions illustrates that fairness cannot be reduced to a concise mathematical definition. The choice of the appropriate assessment criteria remains quiet cumbersome <cit.>.



 §.§ Post-processing
 Post-processing steps regard the model's deployment and interpretation. There is also interaction of the AI model with the final users and this includes further learning and further development. The output of the model can be used as a new input to refine (e.g., through re-training) and (re)evaluate the algorithm creating what is known as "feedback loop". Without the feedback loops, AI systems would not be able to adapt to changing environments or improve their performance. However, AI algorithms can get influenced by their output, reinforcing and magnifying biases over time in several ways <cit.>. This self-perpetuating cycle of unfair decisions is resembled in literature to the butterfly effect <cit.>. Different types of biases are connected but understanding the dynamics and the ways that various components are interconnected in such complex systems is quite cumbersome <cit.>.  Systems should undergo rigorous evaluation and monitored on an ongoing basis in the intended domain and the relevant risks have to be communicated <cit.>. 

Harmful action 1: Use of a one-size-fits-all model. 

This becomes harmful when a model is used for data in which there are underlying groups that should be considered differently. For instance, a model that is trained with a dataset composed of pictures of cats, dogs, and tigers, is used to predict the weight of the animal in an image. Using “dogs” or “felines" as labels can be misleading, since tigers and cats have different weights. A single model is unlikely to be best-suited for any group in the population, even if these groups are equally represented in the training data. 
Heuristics mapping: 
Misinterpretation of statistical information is behind this action. Researchers collect statistical data in order to make generalizations from a sample to the population and not the other way around. When it comes to individuals, conclusions are drawn based on data collected at the group level. Stereotypes are usually claimed for a group, and then all members of this group are supposed to possess a set of essential characteristics. The representativeness heuristic makes people believe that a stereotype is true and all members of a group possess a number of essential characteristics <cit.>. Conclusion bias also affect this way of thinking, as people instead of doing the hard work of finding the best evidence, they just jump to a premature conclusion. Aggregated data often presents a simplified view, reducing complexity and cognitive load. When people rely on their feelings towards a group, they may overlook the diversity and individual differences within the group, resulting in incorrect assumptions about its individual members <cit.>. 
Reflections: The resulting bias at this phase of the AI lifecycle is the aggregation bias. Ecollogical fallacy is a cognitive bias in this category, that refers exactly to the inference that is made about an individual based on their membership in a group <cit.>. 

Harmful action 2:  Use of an AI model in ways not intended by developers.
 
During a system's performance validation it should be checked (among others) whether it is used in unintended ways or not<cit.>. For instance, a risk assessment model that is built to predict the likelihood of a criminal committing a crime in the future, should not be used to determine the length of a defendant's sentence <cit.>. Another common example in this category regards the distinction of AI systems between automated decision-making (ADM), where the system makes a decision automatically without any human involvement, and decision-support, where the system just supports a human decision-maker <cit.>. In case people put their faith in automated decision making whilst a model is not built for this, the consequences can be disastrous. 
Heuristics mapping: It's human's cognition that should be understood and taken into consideration in this collaborative decision-making.Anchoring bias comes up when humans form a skewed perception due to an anchor, which is in this case the AI decision. As an example, when it comes to approaches that are more quantitative, subject matter experts may inadvertently activate a bias like the McNamara fallacy and take advantage of an AI system to take the pressure off of them in favor of the automation's objectivity <cit.>.Confimation bias could also be the reason for decision makers to favor suggestions from automated decision-making systems that align with their preexisting beliefs (and act as an anchor) and disregard decisions that contradict them, but could otherwise turn out to be more appropriate <cit.>. In case the suggestions align with their beliefs, the adjustment required is minimal, and the suggestions are easily accepted. Conversely, if the suggestions contradict their beliefs, the required adjustment is larger, leading to the dismissal or of these suggestions. Unfortunately, decision makers often use decision-support systems without fully understanding how they work. Overconfidence makes people overestimate their understanding of a domain but experts that make decisions are not necessarily familiar with machine learning, data science, or other fields associated with AI design and development. AI remains largely opaque, as advanced mathematics need to be understood by experts<cit.>. The affect heuristic can also drive decisions at this phase. The more human-like a system appears, the more likely it is that users attribute more human traits and capabilities to it. Automated systems often create a sense of safety and comfort, reducing anxiety and leading to higher reliance. The emotional comfort provided by these systems can overshadow critical assessment. Anthropomorphising systems like conversational agents (CAs) is a common example of systems that lead to overreliance and unsafe use <cit.>. 


Consequently, the Dunning-Kruger effect may also come up in such cases, making people overestimate their understanding of the domain. As discussed in Section 2, excessive coherence, that is, belief in the story, leads to overconfidence. Unfortunately, AI remains largely opaque, as advanced mathematics need to be understood by experts<cit.>. 

Reflections: The resulting bias at this phase is the deployment bias, that describes the discrepancy of how a model is used in comparison to what the model is designed to do. A system can be evaluated as if it were fully autonomous leading to automation bias that makes decision makers overestimate its credibility <cit.>. 






Harmful action 3:  Provide a biased feedback.

AI systems change over time. The output of the model is used as a new input to refine (e.g., through re-training) and (re)evaluate the algorithm creating what is known as "feedback loop". Without the feedback loop, AI systems would not be able to adapt to changing environments or improve their performance. However, AI algorithms can get influenced by their output, reinforcing and magnifying biases over time. This can result in a self-perpetuating cycle of unfair decisions and is resembled in literature to the butterfly effect <cit.>.
Reflections:
Biases can be perpetuaed due to feedback loops in several ways <cit.>. For example, feedback loops can change the sample for whom a decision is made, affecting representation bias. Recent studies found that GPT detectors consistently misclassify non-native English writing samples as AI-generated. Essays authored by non-native writers inherently exhibit reduced linguistic variability compared to those authored by native speakers, leading to their misclassification as AI-generated text. Things can get even worse in case the model receives input and training data from native speakers (as it may be avoided in cases of non-natives' evaluation). Without intervention, the model can become even less accurate for non-natives authors. This output influences the training data and unintentionally exacerbate systemic biases by a feedback loop <cit.>. As another instance, feedback loops can also alter the inherent properties of an individual, affecting historical bias. For instance, a recommendation system may influence a user's opinion (e.g. regarding a political issue) by serving specific news articles <cit.>.  People scan results in the order in which they are presented and chose to click more on first results than on later results. This is an example of availability bias as people prefer the piece of information that is easily accessible without making an effort to look for other, potentially more significant information. As this behavior may reflect higher consumption of the top positions of a piece of content, the system may learn that this content is more successful and relevant, which in turn increases the likelihood that it will be served in top positions in the future <cit.>. Several types of biases are connected but understanding the dynamics and the ways that various components are interconnected in such complex systems is quite cumbersome <cit.>. Diversity, equity, and inclusion efforts can help development teams understand the ways a system is likely to impact a wide variety of users and how such users might interact with the system in practice, leading to potential harms or benefits across different groups. Systems should undergo rigorous evaluation and monitored on an ongoing basis in the intended domain and the relevant risks should be communicated <cit.>. 




§ TOWARDS A HUMAN AND AI BIASES TAXONOMY - CONCLUSIONS

In our technology-laden lives, AI even carefully designed to be fair, remains biased with human-societal harms spotted over the AI lifecycle. The full spectrum of risks posed by biases to AI is not encompassed sufficiently in the current computational and accuracy-based fair AI solutions. Humans and AI synergetic future is essential and this work offers a systematic mapping among the ways humans' heuristics lead to wrong or sub-optimal decisions which then are reflected in the AI detected computational biases. In  Figure 1, this heuristics-computational bias mapping, serves as an initial taxonomy which will offer new line of FairAI hypothesis setting and testing. With hundreds of cognitive biases that interfere with how people process data and think critically and several frameworks developed to identify computational biases in AI, this work can't be exhaustive 
However, it constitutes a scientifically evidenced human and AI biases alignment summary, explanatory of the ways biases are perpetuated and amplified, and sheds light to prominent risks and vulnerabilities to consider when designing, developing, deploying, and evaluating FairAI solutions. Our analysis does not prove that other researchers’ explanations or assumptions are wrong. Indeed, the biases in FairAI pipelines may be a result of several factors, and even deeper study will need to explore the complicated and complex biases "iceberg”. In summary, we claim that we offer a stepping stone to widen the study of human biases role in FairAI by revealing possible cognitive-computational biases intensities and inter-dependencies, largely uknown up to now. Our ongoing work on FairAI focuses on extending this mapping to a more holistic human-inclusive taxonomy which will be theoretically grounded and experimentatlly tested, under varying and bias-sensitive constraints posed in several critical FairAI domains. 



§ TOWARDS A HUMAN AND AI BIASES TAXONOMY - CONCLUSIONS

In our technology-laden lives, AI systems even carefully designed to be fair,  remain biased with human-societal harms spotted over the AI lifecycle. Current computational and accuracy-based fair AI solutions fail to encompass sufficiently the full spectrum of risks posed by biases. Recognizing the necessity of a future where humans and AI work in synergy, this work offers a systematic mapping among the ways humans' heuristics lead to wrong or sub-optimal decisions which then are reflected in the AI detected computational biases.
Biases intertwine, causing cascading effects on the final AI system's performance. Biases in the collected datasets can lead to under-representation or exclusion of certain groups, resulting in discriminatory behaviors. An AI model,though, does not merely reflect existing biases in the data; humans’ subjective choices regarding the act of selecting or computing features and model design choices during development can lead to measurement and algorithmic bias. Consequently, if a model is not assessed on a diverse dataset in real-world settings, some populations may be disproportionately impacted. Additionally, evaluation biases can emerge if inappropriate metrics are used. Finally, aggregation and deployment biases may arise if end-users are not adequately trained or supported regarding the right use of the model, further perpetuating a cycle of biased feedback loops.
These harmful actions stem from humans’ heuristics that result in computational biases. The representativeness and availability heuristic seem to be responsible for the most types of computational biases like representation, measurement, algorithmic and evaluation bias. The representativeness heuristic is also responsible for actions that result from people's bigotry and result in historical bias. The role of affect, mood and emotion should also not be neglected. This automatic response from the intuitive system can generate judgements able to generate measurement, aggregation, deployment and even algorithmic bias. Anchoring and adjustment seems to be more manifest when a model is evaluated or deployed as an “anchor” becomes available.
Figure 1 illustrates a preliminary taxonomy of this heuristics-computational bias mapping, offering a new line of FairAI hypothesis setting and testing. With numerous cognitive biases that interfere with how people process data and think critically and several frameworks developed to identify computational biases in AI <cit.>, this work can't be exhaustive. Nonetheless, it constitutes a scientifically evidenced human and AI biases alignment summary, explanatory of the ways biases are perpetuated and amplified, and sheds light to prominent risks and vulnerabilities to consider when designing, developing, deploying, and evaluating FairAI solutions. Our analysis does not prove that other researchers’ explanations or assumptions are wrong. Indeed, the biases in FairAI lifecycle may be a result of several factors, and even deeper study will need to explore the complicated and complex biases "iceberg". In summary, we claim that we offer a stepping stone to widen the study of human biases role in FairAI by revealing possible cognitive-computational biases intensities and inter-dependencies, largely uknown up to now. Our ongoing work on FairAI focuses on extending this mapping to a more holistic human-inclusive taxonomy which will be theoretically grounded and experimentatlly tested, under varying and bias-sensitive constraints posed in several critical FairAI domains.




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§ CITATIONS AND BIBLIOGRAPHIES


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  Some examples.  A paginated journal article <cit.>, an
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  and (Case 3) a patent <cit.>, work accepted for
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  couple of citations with DOIs:
  <cit.>. Online
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  Artifacts: <cit.> and <cit.>.



§ ACKNOWLEDGMENTS


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§ APPENDICES


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§ MULTI-LANGUAGE PAPERS


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§ SIGCHI EXTENDED ABSTRACTS


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To Robert, for the bagels and explaining CMYK and color spaces.





ACM-Reference-Format









§ RESEARCH METHODS




 §.§ Part One


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 §.§ Part Two


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§ ONLINE RESOURCES


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enim maximus. Vestibulum gravida massa ut felis suscipit
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dui. Etiam sagittis eleifend elementum.

Nam interdum magna at lectus dignissim, ac dignissim lorem
rhoncus. Maecenas eu arcu ac neque placerat aliquam. Nunc pulvinar
massa et mattis lacinia.



 Paper Title: The Realizability of Revision and Contraction Operators in Epistemic Spaces.


    

Determination of electron screening potential of ^6Li(p,α)^3He reaction using Multi Layer Perceptron based neural network
     D. Chattopadhyay^1
    August 5, 2024
=========================================================================================================================

plain

§ ABSTRACT 
















This paper studies the realizability of belief revision and belief contraction operators in epistemic spaces. We observe that AGM revision and AGM contraction operators for epistemic spaces are only realizable in precisely determined epistemic spaces. We define the class of linear change operators, a special kind of maxichoice operator. When AGM revision, respectively, AGM contraction, is realizable, linear change operators are a canonical realization.




§ INTRODUCTION

















The area of belief change deals with the adaption of agents’ beliefs in the light of new information from a fundamental perspective. 
A general novel framework for considering belief change is the framework of belief change operators for epistemic spaces <cit.>.
In this framework, belief change happens on a specific defined set of epistemic states. 
The belief change operators are global mathematical objects that define


how each state changes in the light of arbitrary new information.
An advantage of this framework is that it recognizes the varying nature of epistemic states of individual agents, which is given, e.g., due to the individual capabilities of the agents.
Many other frameworks do not allow such a differentiation, because they (implicitly) assume that all belief sets are available.

In this paper, we study the realizability of belief change operators: the question of whether there exists a belief change operator that satisfies a given set of postulates under a given set of assumptions.
This problem been addressed for many types of belief change operators in various frameworks <cit.> and are an ongoing research subject, but has been not considered for the expressive framework of epistemic spaces.


The generality of the framework  for epistemic spaces and the global nature of belief change operators in this framework has consequences for the problem of realizability.
To check realizability, one must consider all belief changes on all epistemic states (available in the considered epistemic space) together and decide if all those belief changes do not interfere with each other.
This is different to deliberations about the realizability of belief change operators considered in many other frameworks, where a realizability is about the changes in one particular belief set or sequences of changes. 










In the following, we list the main content of this paper, which also includes the main contributions of this submission:

    
  * [Non-Existence] 

    
    
    Our first observation is that AGM revision and AGM contraction operators <cit.> for epistemic states <cit.> do  not exist in all epistemic spaces.
    Furthermore, the corresponding epistemic spaces do not coincide.


    
  * [Realizability of Revision and Contraction] A precise characterization of those epistemic spaces is given, where any AGM revision operators, respectively, any AGM contraction operators, exist at all.
    
    
  * [Realizability of Operators] 
    
    We consider then realizability of concrete belief revision and contraction operator approaches: full meet, maxichoice <cit.> and newly defined linear change operators.

    For contraction, we obtain the realizability of all these operators coincides with realizability of contraction.
    When considering realizability of revision operators, we observe that realizability of general revision, linear revision and maxichoice revision coincide.
For full meet revision 

 the realizibility is different. 

    
  * [Non-Interdefinability of Revision and Contraction] 

     A consequence of our investigation is that there are epistemic space where one of AGM revision and AGM contraction is realizable and the other type of operator not. This shows that in some epistemic spaces revision and contraction are not interdefinable, which is an extension to a result obtained by  ().











    
    
    
    


    
    


    
    






























We have proofs for all results given in this paper, which can be found in the accompanied
	supplementary material. 









    
    
    
    


    
    


    
    


 


§ PROPOSITIONAL LOGIC, MINIMA AND ORDERS













Let Σ={a,b,c,…} be a finite set of propositional atoms and let  be a propositional language over Σ. 







The set of propositional interpretations is denoted by Ω.











Propositional entailment is denoted by  and the set of models of α with α.
For L⊆,  entailment and model sets are defined as usual, i.e., L=⋂_α∈ Lα and Lβ if for all α∈ L holds αβ.
We define Cn(L)= {β| L β} and we define L+α = Cn(L∪{α}). Furthermore, L is called deductively closed if L = Cn(L) and  is the set of all deductively closed sets.







Given Ω'⊆Ω and a total preorder ≤⊆Ω×Ω (total and transitive relation), we denote with 


min(Ω',≤)={ω∈Ω' |ω≤ω'  for all ω'∈Ω' }
the set of all minimal interpretations of Ω' with respect to ≤.





A linear order ≪⊆Ω×Ω is a total preorder that is antisymmetric.









 





§ EPISTEMIC SPACES, REVISION AND CONTRACTION



We consider the background on epistemic spaces, AGM revision and AGM contraction.
In this work, we model agents by the means of logic. 
Deductive closed sets of formulas,  which we denote from now as belief set, represent deductive capabilities; agents are assumed to be
perfect reasoners.

The interpretations represent worlds that the agent is capable to imagine.
The following notion describes the space of epistemic possibilities of an agent's mind in a general way.


    
    A tuple 
    =
    ⟨,⟩ is called an epistemic space if  is a non-empty set 
    
    and : →.


We call the elements of  epistemic states and use Ψ as shorthand for Ψ. 
Definition <ref>  differs from the definition given by  () insofar that we do not exclude inconsistent belief sets and forbid emptiness of .
Belief change operators for an epistemic space  are global objects, functions on all epistemic states in the mathematical sense.




	Let =
	⟨,⟩ be an epistemic space.
	A belief change operator for   is a function ∘ : ×→.






In general, operators from Definition <ref> could behave arbitrarily, and, of course, for considering revision and contraction, we have to add additional constraints, which we will consider in the following.


AGM Belief Revision for Epistemic Spaces
Revision operators incorporating new beliefs into an agent's belief set, consistently, whenever this is possible.
We use an adaption of the AGM postulates for revision <cit.>

for epistemic states <cit.>, which is inspired by the approach of   ().
    
    
    
    
    
    
    
    
    [3]
    
        Let = ⟨,⟩ be an epistemic space.

        A belief change operator  for  is called an (AGM) revision operator  for  if the following postulates are satisfied <cit.>: 
        
        
            
        
        
        
            
  (R1pstl:R1) 
            
                α∈Ψα
                
            
  (R2pstl:R2) 
            
                Ψα = Ψ + α if Ψ + α is consistent
                
            
  (R3pstl:R3) 
            
                If α is consistent, then Ψα is consistent
                
            
  (R4pstl:R4) 
            
                If α≡βΨα = Ψβ
                
            
  (R5pstl:R5) 
            
                Ψ (αβ)⊆Ψα+β
                
            
  (R6pstl:R6) 
            
                If Ψα + β is consistent,
then Ψα+β⊆Ψ (αβ)
                
            
            
            
            
            
            
        [3]
        
        
            
                
            
            
            
            
            
            
            
            
        
    
    
    
    AGM revision is well-known for aiming at establishing a minimal change of the prior beliefs when revising. 
    This is carried mainly by (<ref>) with respect to the beliefs of an agent.
    Moreover, (<ref>) and (<ref>) are postulates about rational choice that ensure relational minimal change <cit.>; an interpretation that is also used in other areas <cit.>.
    For a detailed discussion of the postulates (<ref>)–(<ref>) we refer to Gärdenfors ().
    In the remaining parts of this paper, we sometimes write revision operator instead of AGM revision operator.
    
    AGM Belief Contraction for Epistemic Spaces
    Contraction is the process of withdrawing beliefs, without adding new beliefs.


    
    Postulates for AGM contraction <cit.> where adapted by  () to the setting of propositional logic.
    An adapted version of these postulates for AGM contraction operators for epistemic spaces from  ()  is given in the following.
    
    
        Let = ⟨,⟩ be an epistemic space.
        A belief change operator  for  is called an (AGM) contraction operator for  if the following postulates are satisfied:
        
            
  (C1pstl:C1)  Ψα⊆Ψ
            
            
  (C2pstl:C2) If α∉ΨΨ⊆Ψα
            
            
  (C3pstl:C3) If α≢⊤α∉Ψα
            
            
  (C4pstl:C4) Ψ⊆Ψα + α
            
            
  (C5pstl:C5) If α≡βΨα = Ψβ
            
            
  (C6pstl:C6) Ψα∩Ψβ⊆Ψ  (αβ)
            
            
  (C7pstl:C7) If β∉Ψ  (αβ),
then Ψ  (αβ)⊆Ψβ
        
        
    
    As in the case of revision, AGM contraction is well-known for aiming at establishing a minimal change of the prior beliefs when contracting, which is carried mainly by (<ref>), (<ref>), (<ref>) and (<ref>). 
    Again, we refer to Gärdenfors () for a detailed discussion of the postulates (<ref>)–(<ref>).

    In the remaining parts of this paper we sometimes write contraction operator instead of AGM contraction operator.
    












    



 













§ REALIZABILITY OF CONTRACTION OPERATORS



We define realizability of AGM contraction as notion that captures whether there is  any contraction  operator.

   We say that AGM contraction is realizable in an epistemic space , if there exists an AGM contraction operator for .


The following proposition fully captures those epistemic spaces  for which an AGM contraction operator for  exists.

	Let = ⟨,⟩ be an epistemic space.
	AGM contraction is realizable in  if and only if  satisfies:
	
		
  (ZCpstl:AGM-ES3) For each Ψ∈ and for each M ⊆Ω with Ψ⊆ M
 there exists Ψ_M∈ such that Ψ_M=M.
	


     
	We start by showing that the existence of  implies satisfaction of (<ref>).
	
	For each ω∈Ω let  φ_ω be a formula such that φ_ω=Ω∖{ω} holds. 
	Let Ψ∈ be an arbitrary epistemic state and M ⊆Ω be such that Ψ⊊ M holds.
	In the following let M∖Ψ ={ω_1,…,ω_n }.
	Using Proposition <ref> we obtain that Ψ_M=(((Ψφ_ω_1)φ_ω_2 ) …) φ_ω_n is an epistemic state with Ψ_M=M.
	
	For the remaining direction, let  be a (non-empty) set of epistemic states that satisfies (<ref>).
	We construct a <ref> contraction operator for .
	Let ≪ be an arbitrary linear order on Ω, and for each ω∈Ω let Ψ_{ω} denote a uniquely chosen epistemic state from  such that Ψ_{ω}=Ψ∪{ω}. 
	
	Let  be the belief change operator  for  given as follows:
	
    Ψα = Ψ   α∉Ψ
    Ψ_α≪

	Because  satisfies (<ref>), we have that for each Ψ∈ and α∈ the epistemic state Ψα exists, i.e., Ψα∈.
	Now let Ψ↦≤_Ψ be defined for each Ψ∈ as follows (see also Proposition <ref>):
	
    ≤_Ψ = (≪∖ (Ω×Ψ)) ∪  (Ψ×Ω).

	Indeed, Ψ↦≤_Ψ is a faithful assignment. Moreover, Ψ↦≤_Ψ is <ref> with .
	By Proposition <ref>, we obtain that  is a contraction operator for .
	
	
	
	
	
	
	
	
	
	
	
	
	
	










The postulate (<ref>) describes that agents are always capable of being more undecided about the state of the world. 
Clearly, Theorem <ref> implies that contraction operators do not exist in every epistemic space. 
For specific types of contraction operators, the realizability could be even more strict.
To that end, we now define some basic types of contraction operators for epistemic states that are inspired by classical approaches to contraction <cit.>.

    Let = ⟨,⟩ be an epistemic space and let  be a contraction operator for .


        We say  is a full meet contraction operator for  if for all Ψ∈ and α∈ holds:
        
    Ψα=Ψ   Ψ∩α≠∅
    Ψ∪α


        We say  is a maxichoice contraction operator for  if for each Ψ ∈ exists a linear order ≪_Ψ such that for all α ∈ holds:  
        
    Ψα = Ψ   Ψ∩α≠∅
    Ψ∪α≪_Ψ











Note that presupposing existence of  in the beginning of Definition <ref>, guarantees implicitly the existence of the epistemic states used by , as otherwise,  would not exist.
Hence, in Definition <ref>, we do not have to deal with the question of realizability. 


For a maxichoice contraction operators for epistemic states, for each epistemic state Ψ, the linear order ≪_Ψ could be different. We denote maxichoice contraction operators that are based, globally, on a single linear order as linear contraction operators:

    Let = ⟨,⟩ be an epistemic space.
    A  contraction operator for  is called a linear contraction operator for  if  is a maxichoice contraction and there is a linear order ≪ on Ω such that for all Ψ∈ holds ≪_Ψ =  ≪ for ≪_Ψ from Definition <ref>.

Next, we consider an example.
 
    Let Σ={ a } and thus Ω={a,a}.
    Consider the following epistemic space ^E<ref> = ⟨,⟩ with ={Ψ_a, Ψ_a,  Ψ_⊤} and  is given by:
    
    Ψ_a    = {a}   Ψ_a    = {a}   Ψ_⊤    = Ω

    The set  contains only epistemic states for which the beliefs are consistent, i.e., Ψ≠∅ for all Ψ∈.
    Let  be a belief change operator for ^E<ref> given as follows for all Ψ∈ and α∈:
    
    Ψα = Ψ_⊤   α∈Ψ
    Ψ

    	For a graphical representation of  consider Figure <ref>.
    	One can check that  is a linear contraction operator, because the linear order ≪ given by a ≪a gives rise to this operator.













The following proposition points out that, when AGM contraction is realizable, i.e., there is an AGM contraction operators, then there is also an instance of all the concrete contraction operators considered here.

	Let  be an epistemic space. The following statements are equivalent:
	
		
  * AGM contraction is realizable in .
        
  * There exists a linear contraction operator  for .
        
  * There exists a maxichoice contraction operator  for .
        
  * There exists a full meet contraction operator  for .

	

In the next section, we will see that for revision the situation is much more complex, as realizablity does not carry over between different types of revision operators.
 


§ REALIZABILITY OF REVISION OPERATORS












	
		
		
		
	


	
		
		
		
	







	
		
		
		
	







Now, we will determine the conditions of realizability of revision operators and specific types of revision operators.



Realizability of AGM revision is defined analogously to Definition <ref>.

	We say that AGM revision is realizable in an epistemic space , if there exists an AGM contraction operator for .

The next theorem characterizes exactly those epistemic spaces that permit existence of revision operators.


	Let = ⟨ , ⟩ be an epistemic space. AGM revision is realizable in  if and only if   satisfies:
	
		
  (ZR1pstl:AGM-ES1) For all ω ∈ Ω there is some Ψ_ω ∈ with Ψ_ω = {ω}.
		
  (ZR2pstl:AGM-ES2) For all Ψ∈ and for all M⊆Ψ there exists Ψ_M∈ with Ψ_M=M.
		
	



     We start by showing that the existence of  implies satisfaction of (<ref>) and (<ref>).
	From satisfaction of (<ref>) and (<ref>) we obtain satisfaction of (<ref>). Therefore, pick an arbitrary epistemic state Ψ∈ and a formula φ_ω such that φ_ω={ω}. 
	Because of (<ref>) and (<ref>) there exists some epistemic state Ψ_ω∈ such that Ψ_ω={ω} and Ψφ_ω=Ψ_ω.
	For satisfaction of (<ref>) consider some Ψ∈. 
	For each M⊆Ψ let φ_M be such that φ_M=M. Clearly, because of (<ref>), revision of Ψ by φ_M yields an epistemic state Ψφ_M such that Ψφ_M=M is satisfied.
	
	For the remaining direction, let  be a (non-empty) set of epistemic states that satisfies (<ref>) and (<ref>).
	Let ≪ be an arbitrary linear order on Ω and for each M ⊆Ω let Ψ_M denote a uniquely chosen epistemic state from  such that Ψ_M=M (as far as such an epistemic state Ψ_M exists in ). 
	
	We construct a revision operator  as follows:
	
    Ψα = Ψ_Ψ∩α   Ψ∩α≠∅
    Ψ_α≪

	Observe that, because of (<ref>), the epistemic state Ψ_Ψ∩α exists whenever Ψ∩α≠∅ holds.
	Moreover, because ≪ is a linear order, we have, for every consistent α, that α≪ is a singleton set, and thus, by (<ref>) the epistemic state Ψ_α≪ exists. For inconsistent α, the epistemic state Ψ_α≪  exists by (<ref>) (and non-emptiness of ). In summary, for every Ψ∈ and α∈ the epistemic state Ψα exists.
	
	We show satisfaction of (<ref>)–(<ref>) by .
	The postulates (<ref>)–(<ref>) are immediately satisfied due to the construction of .
	For (<ref>) observe that the behaviour of  by input α is only defined via models of Ψ and α.
	Consequently, equivalent input formulas are treated equivalently.
	If Ψα∩β is inconsistent, then (<ref>) and (<ref>) are immediately satisfied.
	In the following assume that Ψα∩β is consistent.
	For the case of Ψα∩Ψ≠∅, we obtain (<ref>) and (<ref>) from (<ref>).
	For the case of Ψα∩Ψ=∅, we have by construction that Ψα is a singleton set, i.e., Ψα={ω}.
	Consequently, from consistence of Ψα∩β we obtain ω∈β.
	From minimality of ω with respect to ≪, we obtain that α∩β≪={ω}. 
	We obtain satisfaction of (<ref>) and (<ref>).



The postulates (<ref>) and (<ref>) characterize epistemic spaces which have revision operators.
Figuratively, (<ref>) describes  that the agent is capable of imagining that the real world is as described by exactly one interpretation; 
(<ref>) describes that if an agent can consider a set of interpretations as possible, then this is true for every subset thereof.

A consequence of Theorem <ref> is that there are epistemic spaces where no revision operator exist at all.



As for contraction, we define types of revision operators, for which we will also consider the realizability.

    Let = ⟨,⟩ be an epistemic space and let  be a revision operator for .


    	We say  is a full meet revision operator for  if for each Ψ∈ and α∈ holds:
            
    Ψα=Ψ∩α   Ψ∩α≠∅
    α


    	We say   is a maxichoice revision operator for  if for each Ψ ∈ exists a linear order ≪_Ψ such that for all α ∈ holds: 
        
    Ψα = Ψ∩α   Ψ∩α≠∅
    α≪_Ψ











We denote maxichoice revision operators that are based, globally, on a single linear order as linear revision operators.

	Let = ⟨,⟩ be an epistemic space.
	A  revision operator for  is called a linear revision operator for  if  is a maxichoice revision and there is a linear order ≪ on Ω such that for all Ψ∈ holds ≪_Ψ =  ≪ for ≪_Ψ from Definition <ref>.














We consider an example epistemic space with a maxichoice revision operator, yet no full meet revision operators.

	Let Σ={ a,b } and thus Ω={ab, ab, ab, ab}.
	Consider the following epistemic space ^E<ref> = ⟨,⟩ with ={Ψ_ω|ω∈Ω}∪{Ψ_} and  is given by:
	
    Ψ_ω    = {ω}   Ψ_    = ∅

	Let ≪_1 and ≪_2 be two linear orders on Ω given by:
	
    ab≪_1 ab≪_1 ab≪_1 ab      ab≪_2 ab≪_2 ab≪_2 ab

	We employ ≪_1 and ≪_2 to define a maxichoice revision operator  for  given as follows for all ω∈Ω and α∈:
	
    Ψ_ωα    = Ψ_   α=∅
    Ψ_ω   ω∈α
    Ψ_α≪_1   
    Ψ_α    = Ψ_   α=∅
    Ψ_α≪_2

	    For a graphical representation of  consider Figure <ref>.

To see that there is no full meet revision operator for ^E<ref>, we consider the epistemic state Ψ_ and the formula α = b. If _fm were a full meet revision operator for ^E<ref>, then we would obtain Ψ__fmα = { ab, ab}. However, this is impossible as there is no epistemic state Ψ∈ with Ψ= { ab, ab}.

Example <ref> is a witness for the following observation.


	AGM revision is realizable in ^E<ref>, yet there exist no full meet revision for ^E<ref>.


The next proposition exactly determines the realizability of linear revision operators, maxichoice revision operators, and of full meet revision operators.


Let  be an epistemic space. The following statements hold:
    
        
  * AGM revision is realizable in  if and only if there exists a linear revision operator  for .
        
  * AGM revision is realizable in  if and only if there exists a maxichoice revision operator  for .
        
  * There exists a full meet revision operator  for  if and only if the following postulate is satisfied by :

        
            
  (Unbiasedpstl:unbiased) For all belief sets B ⊆ there exists an epistemic state Ψ_B∈ with Ψ_B = B.
        
        



        
    We show the statements (I) and (II) independently.
    
        
  (I). We start with statement (I).
        If there exists a linear revision operator  for , then there exists a revision operator  for .
        For the other direction, consider the proof of Theorem <ref>: the construction for  in this proof is a linear revision operator.
        
        
        
  (II). We continue with statement (II).
        If there exists a full meet revision operator  for , then by Theorem <ref>, (<ref>) and (<ref>) are satisfied by .
        Now observe that, as  exists, for each Ψ∈ and each M with M⊆Ω∖Ψ there exists some epistemic state Ψ_M with Ψ_M=M. This is because  satisfies (<ref>).
        From (<ref>) we obtain some epistemic state with Ψ_∈ with Ψ_=∅. 
        Consequently, for each M⊆Ω∖Ψ_=Ω there exists some epistemic state Ψ_M with Ψ_M=M.
        We obtain that (<ref>) is satisfied by .
        
        For the other direction, assume that (<ref>) is satisfied by .
        Let Ψ_M denote be a unique epistemic state from  such that Ψ_M=M.
        We define the belief change operator  by:
        
    Ψα = Ψ_Ψ∩α   Ψ∩α≠∅
    Ψ_α

        One can easily verify that  is a full meet revision operator.



Due to Proposition <ref> (I), linear revision operators are a canonical type of revision operators. 
Clearly, because linear revision operators are maxichoice revision operators, we obtain Proposition <ref> (II) from Proposition <ref> (I).
Moreover, Proposition <ref> (III) shows that full meet revision operators exist only when the agent is able to believe in any belief sets.




 


§ CONSEQUENCES FOR THE INTERDEFINABILITY OF REVISION AND CONTRACTION



In the classical AGM belief change framework, revision and contraction are interdefinable <cit.>, via the Levi-identity <cit.> and Harper-identity <cit.>.
As observed at first by  (), revision and contraction operators over epistemic spaces are not interdefinable in general.

	There is an epistemic space  such that there exist more[By cardinality; the set of all revision operators for  has strictly more elements than the set of all contraction operators for .] revision operators for  than contraction operators for .


The results of this paper allow further remarks to the matter of interdefinability of revision and contraction.
First, observe that ^E<ref> from Example <ref> contains the epistemic state Ψ_ab with Ψ_ab = { ab }, yet there is no epistemic state Ψ∈ such that Ψ={ ab, ab} holds. Hence, due to Theorem <ref>, there is no contraction operator for ^E<ref>; showing that the situation can be more drastic than Proposition <ref> suggests.

     AGM contraction is not realizable in ^E<ref>, but 
    AGM revision is realizable in ^E<ref>.


    
        Consider  and  from Example <ref>.
        Recall that  is a contraction operator for .
        By Theorem <ref> there exists no revision operator for . 
    
    
Moreover, in specific epistemic spaces a situation that is dual to Proposition <ref> appears, as witnessed by Example <ref>.

	AGM revision is not realizable in ^E<ref>, but 
	AGM contraction is realizable in ^E<ref>.

We obtain the following dual to Proposition <ref>.

	There is an epistemic space  such that there exist more contraction operators for  than revision operators for .







 


§ CONCLUSION AND DISCUSSION


In this paper, we considered the problem of realizability for AGM revision and AGM contraction in epistemic spaces. 
Next, we highlight and discuss some of this paper's results, outline a plan for future work, and identify open problems.

Realizability and Canonical Operators
The main results, Theorem <ref> and Theorem <ref>, show that AGM revision and AGM contraction, are only realizable in specific epistemic spaces.
Our results support that for epistemic spaces, linear contraction and linear revision are conical instances, as they are guaranteed to exist when the respective kind of belief change is realizable at all (Proposition <ref> and Proposition <ref>).
We want to highlight that the observation that linear revisions are a canonical form of revision for epistemic spaces is converse to the observations made when generalizing AGM revision to arbitrary Tarskian logics, where full meet revision operators always exist and, thus, are canonical instances for AGM revision <cit.>.
Not that the setting considered here is more general than in <cit.>, as, e.g., a closure under conjunction over the available epistemic states is not assumed.
Our results on realizability are also different from those of  (), who investigated which logics AGM revision and AGM contraction operators exist. Still, they did not consider the restrictions given by considering epistemic spaces, which leads to different results.



Potential Application
We predict that our realizability results can be used, for instance, to checking beforehand, whether AGM revision or AGM contraction can be applied out of the box in a specific domain. Another application are algorithms that compute, for a given epistemic space , whether a revision or contraction operator for  exists.

Linear Contraction and Linear Revision
Note that linear contraction and linear revision are novel types of operators, which make sense when considering the setting of epistemic spaces. 
Before us,  () already employed linear orders to define belief change operators. However, the difference is that they did not employ linear orders to specify the global behaviour of operators as we did here in Section <ref> and Section <ref>; their definition corresponds to what is here called maxichoice.





Future Work and Open Problems

In future work, we extend our investigations of revision and contraction constrained by iteration postulates <cit.>.
We are also planning to consider the exact relation of belief change in epistemic spaces to hyper-intensional belief revision <cit.> and to belief change in subclassical logics, for which abstract logic <cit.> will be useful, as well as the work by Flouris <cit.> and results in the understanding of revision in Horn logic <cit.>.
As highlighted in this paper, another interesting problem to study is the interrelation of revision and contraction in epistemic spaces. The interested reader may consult  () and  () as a starting point.
An open problem for the area of (iterated) belief change is to consider more expressive logics and explore what are reasonable belief changes in these settings.
The notion of epistemic spaces might be useful for such endeavours. We expect that our realizability results carry over to more expressive settings, and we plan to check whether this conjecture holds.

 

[3]








§ ACKNOWLEDGMENTS

We would like to thank the reviewers for their detailed comments and valuable feedback. 
Especially, we like to thank the reviews for drawing connections to other areas, e.g., for drawing the connection to abstract logics.
This research is supported by the German Research Association (DFG), project number 465447331.
[3]


kr




§ SUPPLEMENTARY MATERIAL










 §.§ Background

In this section, we present some background results that we need for the proofs in the next section.


A semantic characterization of AGM contraction is epistemic spaces is by faithful assignments.

	Let = ⟨,⟩ be an epistemic space.
	A faithful assignment for  is a function Ψ↦≤_Ψ 
	that maps each epistemic state Ψ∈ to a total preorder ≤_Ψ⊆Ω×Ω over  Ω 
	such that Ψ≠∅ implies Ψ=Ω≤_Ψ.

We link contraction operators and faithful assignments by a notion of compatibility.

    Let = ⟨,⟩ be an epistemic space and let  be a belief change operator  for .
    A faithful assignment Ψ↦≤_Ψ (for ) is called contraction-compatible with  if the following holds:
    
    contraction-compatibleΨα = Ψ∪min(α,≤_Ψ)



A characterization in terms of total preorders on epistemic states is given by the following proposition.

    Let = ⟨,⟩ be an epistemic space and let  be a belief change operator  for .
    We have that  is a contraction operator for  if and only if there is a faithful assignment Ψ↦≤_Ψ for  which is <ref> with .




 §.§ Proofs

In this section, we present proofs for the novel results in our paper.



prop:min_es_for_agmcontraction
	Let = ⟨,⟩ be an epistemic space.
	AGM contraction is realizable in  if and only if  satisfies:
	
		
  (<ref>) For each Ψ∈ and for each M ⊆Ω with Ψ⊆ M
 there exists Ψ_M∈ such that Ψ_M=M.
	

 
    We start by showing that the existence of  implies satisfaction of (<ref>).
    
    For each ω∈Ω let  φ_ω be a formula such that φ_ω=Ω∖{ω} holds. 
    Let Ψ∈ be an arbitrary epistemic state and M ⊆Ω be such that Ψ⊊ M holds.
    In the following let M∖Ψ ={ω_1,…,ω_n }.
    Using Proposition <ref> we obtain that Ψ_M=(((Ψφ_ω_1)φ_ω_2 ) …) φ_ω_n is an epistemic state with Ψ_M=M.
    
    For the remaining direction, let  be a (non-empty) set of epistemic states that satisfies (<ref>).
    We construct a linear contraction operator for .
    Let ≪ be an arbitrary linear order on Ω, and for each ω∈Ω let Ψ_{ω} denote a uniquely chosen epistemic state from  such that Ψ_{ω}=Ψ∪{ω}. 
    
    Let  be the belief change operator  for  given as follows:
    
    Ψα = Ψ   α∉Ψ
    Ψ_α≪

    Because  satisfies (<ref>), we have that for each Ψ∈ and α∈ the epistemic state Ψα exists, i.e., Ψα∈.
    Now let Ψ↦≤_Ψ be defined for each Ψ∈ as follows:
    
    ≤_Ψ = (≪∖ (Ω×Ψ)) ∪  (Ψ×Ω).

    Indeed, Ψ↦≤_Ψ is a faithful assignment. Moreover, Ψ↦≤_Ψ is <ref> with .
    By Proposition <ref>, we obtain that  is a contraction operator for .
    
    
    
    
    
    
    
    
    
    
    
    
    
    



col:es_contraction_linear
	Let  be an epistemic space. The following statements are equivalent:
	
		
  * AGM contraction is realizable in .
		
  * There exists a linear contraction operator  for .
		
  * There exists a maxichoice contraction operator  for .
		
  * There exists a full meet contraction operator  for .
		
	


   Clearly, if one of the Statements (II)–(IV) holds, we immediately obtain Statement (I).
   For Statement (I) implies Statement (II), observe that the operator  constructed in the proof of Theorem <ref> is a linear contraction operator.
   Moreover, because any linear contraction operator is also a maxichoice contraction, we obtain that Statement (II) implies Statement (III).
   We show that Statement (I) implies Statement (IV).
   Let =, be an epistemic space that satisfies (<ref>).
   We construct a full meet contraction operator for .
   For each M ⊆Ω let Ψ_M denote a uniquely chosen epistemic state from  such that Ψ_M=Ψ∪ M, which exists due to the courtesy of (<ref>). Let  be the belief change operator  for  given as follows:
   
    Ψα = Ψ   α∉Ψ
    Ψ_α

   The operator  is AGM contraction operator for  because when using the flat order ≤_Ψ = Ω×Ω for every epistemic state Ψ∈, one obtains that Ψ↦≤_Ψ is a faithful assignment <ref> with .
   
   Given the above-mentioned insights on , one can easily check that the conditions for full meet are given in Definition <ref> are met by .





prop:min_es_for_agmrevision
	Let = ⟨ , ⟩ be an epistemic space. AGM revision is realizable in  if and only if   satisfies:
	
		
  (<ref>) For all ω ∈ Ω there is some Ψ_ω ∈ with Ψ_ω = {ω}.
		
  (<ref>) For all Ψ∈ and for all M⊆Ψ there exists Ψ_M∈ with Ψ_M=M.
		
	

 We start by showing that the existence of  implies satisfaction of (<ref>) and (<ref>).
    From satisfaction of (<ref>) and (<ref>) we obtain satisfaction of (<ref>). Therefore, pick an arbitrary epistemic state Ψ∈ and a formula φ_ω such that φ_ω={ω}. 
    Because of (<ref>) and (<ref>) there exists some epistemic state Ψ_ω∈ such that Ψ_ω={ω} and Ψφ_ω=Ψ_ω.
    For satisfaction of (<ref>) consider some Ψ∈. 
    For each M⊆Ψ let φ_M be such that φ_M=M. Clearly, because of (<ref>), revision of Ψ by φ_M yields an epistemic state Ψφ_M such that Ψφ_M=M is satisfied.
    
    For the remaining direction, let  be a (non-empty) set of epistemic states that satisfies (<ref>) and (<ref>).
    Let ≪ be an arbitrary linear order on Ω and for each M ⊆Ω let Ψ_M denote a uniquely chosen epistemic state from  such that Ψ_M=M (as far as such an epistemic state Ψ_M exists in ). 
    
    We construct a revision operator  as follows:
    
    Ψα = Ψ_Ψ∩α   Ψ∩α≠∅
    Ψ_α≪

    Observe that, because of (<ref>), the epistemic state Ψ_Ψ∩α exists whenever Ψ∩α≠∅ holds.
    Moreover, because ≪ is a linear order, we have, for every consistent α, that α≪ is a singleton set, and thus, by (<ref>) the epistemic state Ψ_α≪ exists. For inconsistent α, the epistemic state Ψ_α≪  exists by (<ref>) (and non-emptiness of ). In summary, for every Ψ∈ and α∈ the epistemic state Ψα exists.
    
    We show satisfaction of (<ref>)–(<ref>) by .
    The postulates (<ref>)–(<ref>) are immediately satisfied due to the construction of .
    For (<ref>) observe that the behaviour of  by input α is only defined via models of Ψ and α.
    Consequently, equivalent input formulas are treated equivalently.
    If Ψα∩β is inconsistent, then (<ref>) and (<ref>) are immediately satisfied.
    In the following assume that Ψα∩β is consistent.
    For the case of Ψα∩Ψ≠∅, we obtain (<ref>) and (<ref>) from (<ref>).
    For the case of Ψα∩Ψ=∅, we have by construction that Ψα is a singleton set, i.e., Ψα={ω}.
    Consequently, from consistence of Ψα∩β we obtain ω∈β.
    From minimality of ω with respect to ≪, we obtain that α∩β≪={ω}. 
    We obtain satisfaction of (<ref>) and (<ref>).


prop:nofullmeet
    
    AGM revision is realizable in ^E<ref>, yet there exist no full meet revision for ^E<ref>.


    AGM revision is realizable in ^E<ref> is witnessed by  in Example <ref>. To show the second part of the statement, we make use of Proposition <ref>, and show that (<ref>) is violated by ^E<ref>. Clearly, this is the case, because there is no epistemic state Ψ in ^E<ref> such that Ψ=Ω holds, i.e., that Ψ = ({aa}) holds.


prop:realizefullmeet_linear
	Let  be an epistemic space. The following statements hold:
    
        
  * AGM revision is realizable in  if and only if there exists a linear revision operator  for .
        
  * AGM revision is realizable in  if and only if there exists a maxichoice revision operator  for .
        
  * There exists a full meet revision operator  for  if and only if the following postulate is satisfied by :

        
            
  (<ref>) For all belief sets B ⊆ there exists an epistemic state Ψ_B∈ with Ψ_B = B.
        
           


    
    We consider the Statements (I)–(III) independently.
    
        
  (I). We start with Statement (I).
        If there exists a linear revision operator  for , then there exists a revision operator  for .
        For the other direction, consider the proof of Theorem <ref>: the construction for  in this proof is a linear revision operator.
        
        
  (II). For Statement (II), recall that every linear revision operator is also a maxichoice revision operator.
        Hence, from Statement (I), we obtain one direction of Statement (II).
        If there exists a maxichoice revision operator  for , then there exists a revision operator  for .
        
        
  (III). We continue with Statement (III).
        If there exists a full meet revision operator  for , then by Theorem <ref>, (<ref>) and (<ref>) are satisfied by .
        Now observe that, as  exists, for each Ψ∈ and each M with M⊆Ω∖Ψ there exists some epistemic state Ψ_M with Ψ_M=M. This is because  is a full meet revision.
        From (<ref>) we obtain some epistemic state with Ψ_∈ with Ψ_=∅. 
        Consequently, for each M⊆Ω∖Ψ_=Ω there exists some epistemic state Ψ_M with Ψ_M=M.
        Now recall that Ψ  is fully determined by Ψ_M, i.e., Ψ_M={α∈| M α}.
        We obtain that (<ref>) is satisfied by  as for M⊆Ω there is one epistemic state  Ψ_M such that Ψ_M=M.
        
        For the other direction, assume that (<ref>) is satisfied by .
        Let Ψ_M denote be a unique epistemic state from  such that Ψ_M=M for every M ⊆Ω.
        We define the belief change operator  by:
        
    Ψα = Ψ_Ψ∩α   Ψ∩α≠∅
    Ψ_α

        One can verify that  is a full meet revision operator.





prop:noleviharper_realiz
	AGM contraction is not realizable in ^E<ref>, but 
	AGM revision is realizable in ^E<ref>.


    Consider ^E<ref> from Example <ref>, by Theorem <ref>, there exists a revision operator for ^E<ref>.
    By Theorem <ref> there exists no contraction operator for ^E<ref>. 



prop:noleviharper_morecontraction
	AGM revision is not realizable in ^E<ref>, but 
	AGM contraction is realizable in ^E<ref>.


    Consider ^E<ref> and  from Example <ref>.
    Recall that  is a contraction operator for .
    By Theorem <ref> there exists no revision operator for . 










 







 Paper Title: High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures.








High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures
    

    Xiang Fu1
    Xinpeng Zhang1
    Jixiang Ma1
    Peng Zhao2
    Shuai Lu1
    Xu T. Liu340000-0003-3980-9803This work is not related to the author's position at Amazon.
    
    
    1Nanchang Hangkong University, China
    2Microsoft, China
    3University of Washington, USA
    4Amazon Web Services, USA
    

    August 5, 2024
==================================================================================================================================================================================================================================================================================================================================



§ ABSTRACT 
Convolution is the core component within deep neural networks and it is computationally intensive and time consuming. Tensor data layouts significantly impact convolution operations in terms of memory access and computational efficiency. Yet, there is still a lack of comprehensive performance characterization on data layouts on SIMD architectures concerning convolution methods. This paper proposes three novel data layouts for im2win convolution: NHWC, CHWN, and CHWN8, and introduces a set of general optimization techniques for both direct and im2win convolutions. 
We compare the optimized im2win convolution with the direct convolution and PyTorch's im2col-based convolution across the aforementioned layouts on SIMD machines. The experiments demonstrated that the im2win convolution with the new NHWC layout achieved up to 355% performance speedup over NCHW layout. 
Our optimizations also significantly improve the performance of both im2win and direct convolutions. 
Our optimized im2win and direct convolutions achieved up to 95% and 94% of machine's theoretical peak performance, respectively.



direct convolution, im2win convolution, NHWC layout, CHWN layout, CHWN8 layout 




§ INTRODUCTION


Convolution is the essential component of deep neural networks for computer vision tasks such as feature exaction from large-scale image data<cit.>. It not only comprises 50%-90% of computational operations including convolutional, pooling, ReLU, and fully-connected layers<cit.>, but also consumes more than 90% of the total execution time of many popular neural networks<cit.>. Hence, optimizing convolution operations is crucial for enhancing the performance of neural networks.

Convolution methods can be classified into three main categories based on how they transform the input tensor: direct, im2col-based, and im2win. Direct convolution performs convolution operations directly on the tensor without changing its format<cit.>. This approach avoids extra memory consumption compared to im2col-based and im2win convolutions, but it suffers from nonconsecutive memory access.
Im2col-based convolution transforms the convolution into general matrix-matrix multiplications (GEMM)<cit.>, leveraging optimized Basic Linear Algebra Subprograms (BLAS)<cit.> for excellent performance. It has regular memory access but a significant extra memory footprint, which greatly limits its applicability on memory-constrained devices. 
Previously, we propose a memory-efficient convolution called image-to-window (im2win), which reorganizes the input tensor into a row of dot product windows and flattens the unique elements of these windows into a row that correspond to the convolution operation's receptive fields<cit.>. It provides sequential memory access and data reuse, and thus greatly reduces memory overhead. 

A tensor memory/data layout (referred as layout henceforth) refers to how the data of a tensor is physically arranged in memory. There are commonly three layouts for tensors: NCHW, NHWC and CHWN, where N is the batch size, C is the number of channels, H is the image height, and W is the image width. It significantly impacts convolution operations in terms of memory access, computational efficiency and compatibility with deep learning frameworks<cit.>.




Tensors are stored as one-dimensional arrays in memory, although logically represented as four-dimensional arrays. An input tensor and an im2win tensor in a NCHW layout are illustrated in <Ref>. Different colors represent different channels. In the NCHW layout, the elements in the width dimension are contiguous in memory by prioritizing the width dimension first, followed by height, channel and batch. 
Assuming a stride of 1 and a filter tensor in the NCHW layout with dimensions of 1x3x2x2, the elements in the solid-lined boxes are used to compute the first output element in the output tensor, while the elements in the dashed-lined boxes are for the next. 
Further details will be provided in <Ref>.

In general, the memory efficiency and performance implications of various tensor layouts with different convolution algorithms on single-input, multiple-data (SIMD) architectures have received limited attention.
The previous im2win works optimize the NCHW layout on CPU and GPU but have not tried the NHWC and CHWN layouts<cit.>.
Li et al. reveal the performance impact of the NCHW layout with the im2col-based convolution and the CHWN layout with the direct convolution in different CNN layers, and propose a fast multi-dimension layout transformation algorithm on GPU<cit.>. 
Li et al. optimize the cache utilization of the NCHW and NHWC layouts in DNN training along with DNN pruning on CPU<cit.>.
To date, no performance characterization have been conducted on NCHW, NHWC and CHWN layouts on SIMD architectures with the three convolution methods. 
 
To address aforementioned gaps, we propose three new layouts for the im2win convolution: NHWC, CHWN, and CHWN8,
and propose a set of optimization techniques based on the roofline model for both direct and im2win convolutions.
We compare the optimized im2win convolution with the optimized direct and PyTorch's im2col-based convolutions with the above layouts on SIMD machines.
Our experiments demonstrate that the new NHWC layout on the im2win convolution achieves 11% to 355% performance speedup compared with the NCHW layout. Our proposed optimizations have been empirically validated to enhance the performance of both im2win and direct convolutions. 
Our optimized im2win convolution and our optimized direct convolution achieve up to 95% and 94% of the theoretical peak performance of the machine, respectively.
As the main contributions, this work

1) proposes three novel layouts for im2win convolution.

2) comes up with a set of general optimization techniques that not only can be applied to im2win convolution and but also to direct convolution on different layouts.

3) compares the optimized im2win convolution with the im2col-based and the optimized direct convolutions using four tensor layouts on SIMD machines.





§ PRELIMINARY AND RELATED WORKS




 §.§ Notation

The input of a convolution operation includes an input tensor (ℐ), a filter tensor (ℱ), and an output tensor (𝒪). In these tensors,
N_i is the batch size, s is the stride size, C_i and C_o are the number of input and output channels (also known as feature maps), H_i/f/o and W_i/f/o denote the height and width of a feature map.



 §.§ Tensor Layouts: NCHW, NHWC, CHWN



In the NCHW layout, the input (ℐ), the filter (ℱ), and the output tensors (𝒪) are expressed as ℐ[N_i][C_i][H_i][W_i], ℱ[C_o][C_i][H_f][W_f], and 𝒪[N_i][C_o][H_o][W_o], respectively. The convolution is defined as:

    [ 𝒪_(i, j, m, n)=∑_j=1^C_i∑_m=1^H_f∑_n=1^W_f(ℐ_(i, j, m × s+u, m × s+v). 
    .×ℱ_(j, r, u, v)), ]


In the NHWC layout, the input (ℐ), the filter (ℱ), and the output tensors (𝒪) are expressed as ℐ[N_i][H_i][W_i][C_i], ℱ[C_o][H_f][W_f][C_i], and 𝒪[N_i][H_o][W_o][C_o], respectively. The convolution is defined as:

    [ 𝒪_(i, m, n, j)=∑_j=1^C_i∑_m=1^H_f∑_n=1^W_f(ℐ_(i, m × s+u, m × s+v, j). 
    .×ℱ_(j, u, v, r)), ]


In the CHWN layout, the input (ℐ), the filter (ℱ) and the output tensors (𝒪) are expressed as ℐ[C_i][H_i][W_i][N_i], ℱ[C_i][H_f][W_f][C_o], and 𝒪[C_o][H_o][W_o][N_i] respectively. The convolution is defined as:

    [ 𝒪_(j, m, n, i)=∑_j=1^C_i∑_m=1^H_f∑_n=1^W_f(ℐ_(j, m × s+u, m × s+v, i). 
    .×ℱ_(r, u, v, j)), ]

The definitions (1), (2) and (3) above are all subject to

    j=1,2,..,C_o, m=1,2,..,H_o, n=1,2,..,W_o, 
       i=1,2,..,N_i, u=1,2,..,H_f, v=1,2,..,W_f,
       r=1,2,..,C_i.





 §.§ Convolution Algorithms and Related Works



Direct convolution performs on the original ℐ and ℱ without any tensor transformation.
It has seven nested for loops and an AXPY operation in the innermost loop.
Based on the tensor layouts that direct convolution works with, the AXPY operation needs to read at different indices of ℱ and ℐ, and writes at different indices of 𝒪.

Direct convolution can compute with the original input tensors, so they usually adopt the NCHW layout of raw images. A study shows that, for convolution instances with large C, NHWC layout outperforms NCHW layout<cit.>. Grouping a certain dimension of the input tensors by a fixed size can also enhance the performance of direct convolution, such as NC32HW32 layout<cit.>.
Several works <cit.> have shown that the performance of direct convolution can be greatly improved by designing specific layouts based on the loop ordering of the algorithm on SIMD architecture. 

The im2col-based convolution transforms a convolution operation into a GEMM operation. ℐ[N_i][C_i][H_i][W_i] is processed in N_i batches, each batch contains data ℐ'[C_i][H_i][W_i] (that is, a single image). 
The im2col algorithm flattens the elements of each dot product window of  ℐ' and copies them into a single row of a 2D matrix<cit.>.
In addition to the conventional im2col data transformation algorithm, 
the MEC algorithm compresses the matrix layout, while still enabling the utilization of high-performance BLAS algorithms to perform convolution operations<cit.>.



§ HIGH-PERFORMANCE IM2WIN AND DIRECT CONVOLUTION USING THREE TENSOR LAYOUTS


In this section, we first review three layouts in the context of the direct convolution. Then we present three new layouts for the im2win convolution, following by how to determine the loop ordering based on the layouts and a set of optimizations for the im2win and direct convolutions on SIMD architectures.








 §.§ Motivations for Different Tensor Layouts



An example of direct convolution on an original input tensor in the NHWC layout is illustrated in <Ref>. 
The NHWC layout prioritizes the storage of elements in the last logical dimension—C_i—followed by that of W_i, H_i, and N_i. 
In the NHWC layout, the elements with the same N_i, H_i, and W_i but different C_i are contiguous in memory, which have unit stride access.
Assuming a stride of 1,
the red and green elements (outlined by solid lines, representing the convolutional windows to compute a single output element) in the input tensor are multiplied with the corresponding elements in the filter tensor, and the results are summed up (i.e., an AXPY) to obtain O_00. 
Next, the green and yellow elements (outlined by dashed lines) are used to calculate O_01. This process continues, with the convolutional window moving by the stride length in H_i or W_i, until all elements of the output tensor are computed.


Recall in <Ref>, the NCHW layout prioritizes the storage of elements in the last logical dimension—W_i—followed by that of H_i, C_i, and N_i.
Note that all C_i but not all W_i are used during the AXPY operation to obtain one output element. NCHW has non-unit stride access during the tensor convolution. However, the non-unit stride access may not be harmful, depending on the effects of caching and the access patterns used (determined by the loop ordering).

As shown in <Ref>, the CHWN layout stores elements by prioritizing N_i, followed by W_i, H_i, and C_i in memory. 
Previous research on GPU recommends to use N as the lowest dimension for coalesced memory access and data reuse in registers<cit.>. It has been observed that the performance is sensitive to the value of N.
The elements within the solid-lined boxes are used to compute the first eight output elements, while the elements within the dashed-lined box are used for the next eight output elements. 
This facilitates the use of vector registers for vectorization.
Assuming s=1, the convolutional window moves by one element in both the H_i and W_i dimensions until all output elements are computed.


 













 §.§ Im2win Tensor Transformation on Three Tensor Layouts



In this subsection, we present the im2win tensor transformation on three tensor layouts. The im2win tensor transformation process for the NWHC layout is shown as <Ref>. For other layouts, slight modifications need to be made on it. The im2win transformation flattens the elements of a convolutional window, storing them contiguously in memory and prioritizing them in the C_i dimension, as outlined in <Ref> from <Ref> to <Ref>. 
An example of transforming the original input tensor into an im2win tensor in the NHWC layout is shown in <Ref>. It is important to note that the green elements can be reused between two adjacent windows, which eliminates the need for repetitive storage in memory like the im2col transformation<cit.>.
In the output im2win tensor of <Ref>, the elements involved in each convolution operation are contiguous and more compact in memory, 
improving spatial locality,  cache and SIMD efficiency.

An example of the im2win convolution using the NHWC layout is shown in <Ref>. A naive im2win convolution method is shown as <Ref>. It is similar to the direct convolution, which involves seven nested loops and an AXPY in the innermost loop, 
except prior to that, the im2win convolution performs a tensor transformation upon ℐ to get Î, and ℱ in the NHWC layout is transformed into a NWHC layout to match Î.
All output elements can be computed through the outer four loops of <Ref> from <Ref> to <Ref>.

An im2win tensor in CHWN/CHWN8 layout is shown in <Ref>. The characteristics of C_i, H_i, and W_i in this layout are similar to NCHW, but the CHWN layout prioritizes storage in N_i. 
The CHWN layout's efficiency is constrained by 256-bit vector registers, which process only 8 outputs at once. When N_i > 8, this leads to low cache utilization, as the cache holds unnecessary data for these 8 output calculations.
Motivated by the approach of dividing C_i into blocks in the direct convolution <cit.>, we propose a new CHWN8 layout.
CHWN8 lays 8 N_i in the innermost layer and remaining N_i in the outermost layer, which takes full advantage of the vector registers without sacrificing the cache utilization.
N_i can be set to a multiple of 8 (with padding if necessary), 
CHWN8 layout may provide better data continuity than the NHWC layout when C_i is relatively small.







 §.§ Loop Reordering


In this subsection, we reorder the loops for the im2win and direct convolutions based on different tensor layouts.
Ideally, we arrange the inner loops to access data closer in memory to enjoy the unit stride access as long as possible.
In both direct and im2win convolutions, for NCHW, CHWN, and CHWN8 layouts, as they have the CHW memory access pattern, we use the width of the convolution window as the innermost loop, followed by the height, and the channel (W_f, H_f, and C_i). 
Conversely, for the NHWC layout, the innermost three loops iterate over the channel, the width, and the height (C_i, W_f, and H_f) of the convolution window. 
For the im2win convolution, since the im2win transformation flattens the elements of a convolutional window, we usually coalesce the height and the width layers (W_f, H_f) into a W_f × H_f.

Next we determine the order of the outer four loops. It not only determines the order in which the elements of the output tensor are produced but also influences the order in which the element in the input tensor are accessed. 
The layout mainly affects the inner three levels of the loop order, for the outer four levels the loop order applies to all data layouts.
Recall in <Ref>, the green elements of the input tensor are shared between the convolution windows of adjacent output tensor elements. Therefore, we consider to position the width (W_o) of the traversed output tensor in the fourth layer of loops at <Ref> in <Ref>.
Because the memory access of the input tensor is expensive in a convolution operation, we set the channel (C_o) as the third layer to reduce its access.
Since the batch of the output tensor corresponds to the batch of the input tensor, we place the batch (N_i) in the first layer of the loop. 
Finally we have the order of the outer four loops as NHCW in <Ref>. 



 §.§ Optimizations for the Im2win and Direct Convolutions




In this subsection, we use the Roofline Model<cit.> to determine how to optimize the im2win and direct convolutions on SIMD systems. 
We propose a set of optimizations for both the im2win and direct convolutions and apply the optimizations to them (with a slight modification on the direct convolution). The optimizations are classified into two categories: reducing the memory bottleneck and increasing the arithmetic intensity of the convolution kernel. The former includes hoist, memory alignment, register and cache blocking. The latter includes
loop unrolling, vectorization and FMA instructions, loop coalescing, and parallelization strategies.
We have three level parallelization: Non-Uniform Memory Access (NUMA) level, thread level using OpenMP and instruction level using SIMD.

We present the optimized im2win convolution as <Ref>. 
Since each element of the output tensor in  each batch (N_i) can be computed independently, there is abundant parallelism available<cit.> and this is NUMA-friendly.
Within each batch, the shared elements of adjacent windows can be maximized across the threads.
When the dimension we choose to parallelize is small and the number of CPU cores is large, this leads to a workload imbalance in many core architecture. The solution is to coalesce multiple dimensions/loops into one parallel loop to achieve better load balance. In practice, we find that coalescing two dimensions of the output tensor yields the best load balance. 
Hence, we apply parallel strategies and coalesce N_i and H_o in a parallel loop at <Ref> in <Ref>.

In <Ref>, we hoist three things: the indices of the elements in the 1D array of the tensor, the elements of the input tensor, and the entire filter tensor at <Ref>.
We use register and cache blocking<cit.> at <Ref> to reduce cache misses because the neighboring convolutional windows share duplicate elements of the input tensor, which W_o,b is the blocking size.
Next, we unroll the loop at <Ref> to take advantage of the spatial locality,
because the innermost loop accesses consecutive elements.
Since we are using FP32 and AVX2, we set the loop unrolling size N_vec=8. Finally, we vectorize the input tensor and filter tensor in units of eight to use the FMA instruction in AVX2<cit.> at <Ref>. 

A cache-line is 512 bits on contemporary x86_64 architectures, which is the minimum data quantity that can be fetched from memory to cache. Without memory alignment of the tensor data structure, the CPU needs to issue two memory requests to access an element, because the element may be in the middle of the cache-line. This hurts the performance greatly because not only the CPU has to wait for memory access but also more caches are used. This leads to higher cache miss and lower cache utilization. We store the elements of the tensor in a memory-aligned way using posix_memalign in C during the memory allocation.













§ EXPERIMENTS



 §.§ Experimental Setup

Architectures. We use a server with two Intel® Xeon® Gold 6330 CPUs and 251 GB RAM. Each CPU has 28 physical cores, running at 2.0 GHz, with 48 KB L1d cache, 32 KB L1i cache, 1.28MB L2 cache and 43 MB L3 cache.

Benchmarks We aim to cover the majority of the convolutional layers in commonly used DNNs. 
Hence for our experimental evaluation, we select an state-of-the-art DNN benchmark<cit.> shown in <Ref>, which includes twelve unique convolution layers, conv1-conv12.



Software We compare the direct, im2win, and im2col-based convolutions with four tensor layouts: NHWC, NCHW, CHWN, CHWN8. We intend to compare with the state-of-the-art implementation of direct convolution and the layout proposed in<cit.>, but their implementation is not open-sourced. 
We use the im2col-based convolution in PyTorch 2.1<cit.> with MKL<cit.>. Note that PyTorch only supports the NHWC and NCHW layouts.
Our code is compiled with GCC 9.5.0 compiler and  compilation flags. We use OpenMP 4.0 for parallelization with guided scheduling. 





 §.§ Performance of Different Convolution Algorithms


We run each algorithm 50 times on each benchmark with N_i=128 and report the best runtime. 
<Ref> shows the performance results in TFLOPS and <Ref> shows the memory usage of our high-performance direct convolution and high-performance im2win-based convolution, and the im2col-based convolution using MKL in PyTorch. In <Ref>, the left y-axis shows the performance in TFLOPS, and the right y-axis shows the performance of the machine peak.
Overall, our optimized im2win convolution achieves eight best TFLOPS out of twelve benchmarks; our optimized direct convolution achieves three best TFLOPS out of twelve benchmarks and the im2win convolution achieves close performance on these three; the im2col-based convolution in PyTorch achieves one out of twelve best TFLOPS on conv12. 
All twelve best TFLOPS are all yielded from the NHWC layout across these three methods.
Our proposed optimization techniques are proven effective on both im2win and direct convolutions. Our im2win convolution achieves 95% and 91% of the theoretical peak performance of the architecture on conv5 and conv6 respectively. Our direct convolution achieves 91% and 94% of the theoretical peak performance on conv5 and conv6 respectively. 

With the NHWC layout and performance normalization, 
excluding conv6 and conv12, our im2win convolution achieves between 1.1× and 4.6× performance speedup, and our direct convolution achieves between 1.1× and 3.8× performance speedup against the im2col-based convolution. All the best performance of the im2win convolution on twelve benchmarks is achieved using the NHWC layout on CPU. Our im2win convolution using the NHWC layout outperforms the NCHW layout by at least 11% and up to 355% across all benchmarks, showcasing significant efficiency gains in various scenarios.
For the direct convolution, nine out of twelve best TFLOPS are achieved using the NHWC layout on CPU, the rest three are achieved using the CHWN8 layout.

With the NCHW layout,
our im2win-based convolution achieves between 1.4× and 2.4× performance speedup on all benchmarks, and PyTorch's im2col-based convolution achieves between 1.1× and 7.5× performance speedup (excluding conv7) against the direct convolution. The direct convolution performs poorly on the NCHW layout. 

With the CHWN/CHWN8 layout, im2win_CHWN8 outperforms im2win_CHWN on all benchmarks from 3.7× to 16×; direct_CHWN8 outperforms 2.3× and 8× over direct_CHWN except in conv7. 
This shows our proposed CHWN8 layout overwhelmingly beats the CHWN layout on these two methods.
The direct convolution with the CHWN8 layout 
 performs better than the CHNW, NCHW and NHWC layouts when C_i is small (C_i=3 for conv1, conv2, conv3). Similar results have also been observed in the previous GPU work<cit.>.

In <Ref>, using the same tensor layout, the memory usage of each convolution is the same hence we only annotate one number per method. In all benchmarks, the direct convolution uses the least memory, while the im2col-based convolution consumes the most memory.
On average, the im2col-based convolution has 3.9× more memory usage than direct convolution, and im2win-based convolution has 1.5× more memory usage than direct convolution. The im2win-based convolution uses on average 39% of the memory of the im2col-based method, and in conv5, it uses only 24% of the memory of the im2col-based convolution.




§ CONCLUSION


We proposed three new layouts for the im2win convolution: NHWC, CHWN and CHWN8, and a set of general optimization techniques for both direct and im2win convolutions on SIMD architectures. We applied these optimizations on the im2win and the direct convolutions, and compared with PyTorch's im2col-based convolution on the above layouts along with the NCHW layout. 
Our experiments demonstrated that im2win convolution using the new NHWC layout achieved 11% to 355% performance speedup compared to NCHW layout. 
The proposed optimizations were proven to boost the performance of the im2win convolution and direct convolution. 
Our optimized im2win and direct convolutions achieved up to 95% and 94% of the theoretical peak performance of the machine, respectively.



§ ACKNOWLEDGMENT

This work was partially supported by the NSF OAC-2402542, and the National Natural Science Foundation of China (Grant No. 42261070).

IEEEtran








We leave some information out from the main body of this paper and have them as part of an appendix. Because we consider they are not essential to the main argument but may be useful for readers who want to dive deeper into the topic. 




 §.§ Peak Performance

We use the following formula to calculate the peak-gflops of the server:



    peak_flop/s = (#processors) × (#cores_per_processor) 
    × (clock_speed[1/s]) × (2 ×#FMA_units) ×vector_size[bits]/64



Based on <Ref>, the peak GFLOPS of the server that we use in our experimental evaluation is 3584 GFLOPS.



 §.§ Batch Size Scaling on Different layouts


We perform a strong scaling on the batch size from 32, 64, 128, 256 to 512 with different layouts using the direct convolution and the im2win convolution.
The performance results of the direct convolution with the CHWN, CHWN8, NCHW, and NHWC layouts are shown in <Ref>, <Ref>, <Ref>, and <Ref>, respectively.
The performance results of the im2win convolution with the CHWN, CHWN8, NCHW, and NHWC layouts are shown in <Ref>, <Ref>, <Ref>, and <Ref>, respectively.

From the figures, we can tell that the CHWN layout is most sensitive to the batch size among four layouts. The performance of the direct and im2win convolutions is the best on twelve benchmarks when the batch size is 32 (except for the direct convolution on conv12). Recall in <Ref>, the efficiency of the CHWN layout is constrained by the number of vector registers on the SIMD machines. When N_i>8, this leads to low cache utilization.

With our proposed CHWN8 layouts, the performance of the direct convolution and the im2win convolution exhibits similar patterns on twelve benchmarks, that is, when the channel sizes of the benchmarks are small (C_i=3 for conv1, conv2, conv3), the smaller the batch size is, the better performance the convolutions have on these benchmarks; when the channel sizes of the benchmarks (conv4-conv12) are large, the larger the batch size is, the better performance the convolution have on these benchmarks.

With the NWHC and NCHW layouts, both convolution methods show no obvious evidence that they are sensitive to the batch size across all benchmarks. The performance variance is contributed by the overall dimension of the input/filter tensors, and the loop coalescing which we coalesce the N_i and H_o dimensions into one parallel loop to achieve better load balance. 
























  


 Paper Title: QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression.





QUITO






W. Wang et al.



1. Institute of Computing Technology, Chinese Academy of Sciences 

2. Beijing University of Posts and Telecommunications 








QUITO: Accelerating Long-Context Reasoning through Query-Guided Context Compression
    Wenshan Wang1 Yihang Wang2 Yixing FanCorresponding Author: fanyixing@ict.ac.cn1 Huaming Liao1 Jiafeng Guo1
    
==============================================================================================================



§ ABSTRACT 

In-context learning (ICL) capabilities are foundational to the success of LLM. 
Recently, context compression has attracted growing interest since it can largely reduce reasoning complexities and computation costs of LLM.
In this paper, we introduce a novel QUITO method, which leverages attention of the question over the contexts to filter useless information. Specifically, we take a trigger token to calculate the attention distribution of the context in response to the question. Based on the distribution, we propose three different filtering methods to satisfy the budget constraints of the context length.

We evaluate the QUITO using two widely-used datasets, namely, NaturalQuestions and ASQA. Experimental results demonstrate that QUITO significantly outperforms established baselines across various datasets and downstream LLM, underscoring its effectiveness. Our code is available at <https://github.com/Wenshansilvia/attention_compressor>.



















§ INTRODUCTION

In recent years, LLM has demonstrated notable reasoning and generating capabilities, significantly enhancing the performance of NLP tasks <cit.>. 
However, these models still exhibit limitations in acquiring real-time information and integrating external knowledge <cit.>. 
ICL addresses these deficiencies by including examples and relevant contexts directly within the prompts<cit.>. This approach boost the performance of LLM in downstream tasks without requiring additional training.

To better improve the reasoning ability of LLM, researchers propose different ways to incorporate complex contexts in the input <cit.>. For example, RAG employs an additional searcher to retrieve external relevant documents about the question as the context of inputs, which has attracted lots of attention for both the academia and industry <cit.>. In addition,
Brown et al. <cit.> found that the number of examples has a great impact to the reasoning performance of LLM, where more examples tend to bring better performances <cit.>. 
Moreover, the CoT <cit.> further improves the LLM by involving the reasoning step of each example in the context. 
While these strategies have the potential to significantly improve the capabilities of LLM, they also introduce challenges associated with the increased context length, such as higher inference complexity and costs.





To mitigate this issue, context compression in ICL is becoming a prominent solution. On one hand, reducing the length by removing noise from contexts can improve inference efficiency<cit.>. On the other hand, it meets the input length restrictions of open-source LLMs<cit.> while also reduces the costs associated with accessing proprietary LLMs. 
Several methods <cit.> have been proposed to compress context by estimating the information entropy. This assessment is conducted by utilizing a small external LLM to evaluate the perplexity of individual tokens to identify those that contribute minimal information gain. Tokens that demonstrate low information are subsequently compressed or eliminated. 
However, neglecting the query during compression may result in the inadvertent deletion of key information. 

For the above problem, recent methods such as LongLLMLingua <cit.> adopt a query-aware compression approach by calculating the perplexity of the context conditioned on the query. Despite this advancement, misalignment between compression model and generation model can lead to inconsistencies in determining which tokens are considered to have “low entropy gain”. This discrepancy arises because models may differ in their interpretation and processing of the same information. Our work also scores tokens based on their relevance to the query. However, distinctively, we employ attention metrics rather than perplexity to assess the importance of tokens.

This paper introduces the QUITO method, which strategically selects the context to maintain supporting information by utilizing the attention mechanism. 

Intuitively, the attention mechanism offers a direct method for analyzing the interactions between the question and the context, moving beyond the sole reliance on models' probabilistic uncertainty. This technique facilitates a more precise identification of the information that is most crucial to the current task. 

More importantly, the attention-based filtering can be implemented with small LLM, which improves the computation efficiency.



The main contributions of this study include: 

    
  * This paper proposes a novel context compression method, named QUITO. It utilises self-attention mechanism of Transformers to score the importance of tokens, selecting context relevant to the current query. 
    
    
  * In contrast to earlier methods that requires a compression model with 7 billion or 13 billion parameters, this method achieves superior results using a smaller LLM with only 0.5 billion parameters.
    
  * We conduct extensive experiments on two benchmark datasets, which demonstrate the effectiveness of the proposed QUITO. For example, it surpasses strong baselines with an increase in accuracy of up to 20.2.
    





§ RELATED WORK


In this section, we briefly review two lines of related works, i.e., context compression task and attention mechanism.



 §.§ Context Compression Task

To reduce the length of context, earlier efforts<cit.> opted to summarize and condense retrieved documents using models such as GPT. Other studies <cit.> focused on distinguishing between useful and redundant information within documents, training a model to extract the most valuable sentences. For example, LeanContext <cit.> and FILCO <cit.> train the model to perform sentence-level extraction for the context. Fit-RAG <cit.> scores sub-paragraphs with sliding context windows. RECOMP <cit.> uses a generative model to rewrite extracted candidate sentences, thereby ensuring the coherence and naturalness of the summaries.

Approaches that generate summaries do not allow direct control over the compression ratio, resulting in a growing attention on token and word-level compression techniques in recent times. SelectiveContext <cit.> utilizes self-information within context for token selection. This approach considers perplexity (PPL) to be the representation of the uncertainty of an LLM regarding information carried by contexts. 

Based on <cit.>, LLMLingua <cit.> introduces a two-stage, coarse to a fine, compression method. 
However, these methods fail to consider the relationship between the context and the query. LLMLingua <cit.> further addresses this gap by calculating context-specific perplexity conditioned on the query.

The aforementioned token-level compression methods utilize perplexity as the primary filtering criterion. However, discrepancies often arise between smaller compression models and larger generation models in their assessments of word perplexity, making it challenging to align their judgments on lexical importance.




 §.§ Attention Mechanism

Attention is a significant breakthrough in deep learning, particularly shines in NLP tasks such as translation and summary generation<cit.>. The core concept behind Attention mechanisms involves assigning a specific weight to each input element, such as words or tokens, indicating their relevance to the task at hand. This allows models to focus selectively on more pertinent parts of the input data.

Self-attention, a particular category of the attention mechanism, measures the relationships between all input elements, assessing how each element influences and relates to the others<cit.>. 


Multi-head attention is a key component of the Transformers <cit.>, which improves the model’s capability in capturing diverse correlation patterns.


Recent studies try to use the attention mechanisms within LLMs to accomplish specific tasks. For instance, DRAGIN <cit.> use attention to evaluate the extent to which a given text segment significantly influences subsequent content.
It employs the perplexity of tokens to determine whether to trigger re-retrieval and regeneration processes.

In this paper, we also employ the multi-head attention mechanism to calculate the weights of tokens in context, thereby identifying useless content for answer generation.







§ METHOD





In this section, we introduce the QUITO method in detail. As illustrated in Figure <ref>, QUITO primarily consists of two main components, namely the query-guided self-attention component and the context budget control component. 

In what follows, we will firstly give a formal definition of the task, and then describe each component in detail.




 §.§ Problem Formulation

Given an input p=(s, C, q), where s is the instruction, q is the query, and C={c_i}_i=1^n is the context consisting n documents. Every document c_i={w_i, j}_j=1^L_i contains L_i word. The objective of context compression task can be formulated as:

    min_C̃dist(P(ỹ|s, C̃, q), P(y|s, C, q)),

where ỹ represents the predicted response of the LLM, and y is the ground truth response. dist ( · , · ) is a function that measures the distance of two distributions, such as KL divergence. 

C̃={c̃_̃ĩ}_i=1^n is the compressed context, and  c̃_̃ĩ={w_ j|w_j∈ c_i}_j=1^τ L_i, τ∈ [0, 1]. c̃_̃ĩ is c_i being compressed with ratio at 1/τ, where smaller τ means higher compression ratio.



 §.§ Query-Guided Self-Attention

The query-guided self-attention component aims to estimate the importance of tokens in context by calculating the trigger attention distribution. Firstly, we organize all input by prompt template filling. Then, we calculate the importance of all the input with trigger attention distribution. Finally, we obtain the lexical units importance within the context by context attention reformulating.



  §.§.§ Prompt Template Filling

It is crucial that the compression model fully understands the task at hand and accurately identifies the information most pertinent to the current query. A standard approach involves concatenating the context with the query and subsequently analyzing how tokens within the query attend to tokens in the context. However, in a Transformer decoder-only architecture, the visibility range of each token in the query varies. This variability suggests that tokens positioned later in the sequence more precisely reflect the model’s comprehensive understanding of the task. Given the challenges associated with appropriately weighting tokens at different positions, we propose a novel method that utilize a conversational template and identify a specialized token that encapsulates the compression model’s overall understanding of the task.



  §.§.§ Trigger attention distribution

We embed the context and query into a conversational template, concluding with a signal that prompts the model to initiate response generation. The terminal token within this sequence is designated as a trigger token, serving as an indicator of the model’s assessment of information need after comprehensively understanding the task at hand. Subsequently, we employ a compression model equipped with a multi-head self-attention mechanism to process the completed template and compute the attention that the trigger token accords to the preceding text:

    {α_i|α_i=exp(q_L_total^Tk_i)/∑_j=1^L_totalexp(q_L_total^Tk_j)},

where q_i and k_i are query embedding and key embedding of the i th token, respectively. L_total is the total number of tokens in the completed template. 



  §.§.§ Context attention reformulating

Once the attention allocated by the trigger token to all preceding tokens in the sequence has been determined, the subsequent step involves transforming this attention data into a quantified measure of significance for the lexical units within the context. 
The array {α_i} signifies attention weights, with its length equating to the aggregate of the lengths of the conversational template, the context, and query. Within the scope of this task, it is imperative to concentrate on the attention distributed to the context segment. The attention should not be diluted by the segments pertaining to the template and the query. Consequently, we implement a normalization process, which is designed to ensure that the distribution of attention across various tokens in the context remains unbiased, robust to the disparities in context and query lengths that may exist across different tasks. For the normalization we use softmax function:

    α_i'=exp(α_i+doc_start)/∑_j=doc_start^doc_endexp(α_j), i ∈[1, doc_end-doc_start],

where doc_start and doc_end represent the starting and ending positions, respectively, of the context segment.

We consider words to be the smallest semantic units within a document. In order to perform selection on semantic units, the next step involves transforming scores on token to scores attributed to each individual unit. In other words, we need to transform {α_i'}_i=1^L_doc to {α_i”}_i=1^L, where L_doc is the length of token array {t_i}_i=1^L_doc that belongs to context, and L is the length of word array {w_i}_i=1^L.
A word w_i may consist of one or more tokens. We can formulate a word as w_i={t_j}_j=k+1^k+l, each of which has attention score:

    α_i”=max_k+1 ≤ j ≤ k+lα_j',

where the length of the array {α_i”}_i=1^L is L.



 §.§ Context Budget Control


In the previous section, we have derived a list of words, represented as {w_i}_i=1^L, and the corresponding array of attention weights, {α_i”}_i=1^L. 
This section introduces the filtering methods that satisfy the requirement of the context budget control.




  §.§.§ Phrase Level Filtering

In the process of selecting based on attention scores, it is common to inadvertently overlook words adjacent to those with high attention, referred to as target words, which may also contain crucial knowledge for answering the query. To rectify this oversight and ensure these adjacent words are also considered, we apply a weighted adjustment, allowing them to receive a portion of the attention attributed to the target words. This is accomplished by implementing a Gaussian filter across the word attention array {α_i”}_i=1^L.

    G(x)=1/2πσ^2exp(-x^2/2σ^2)

After the application of the Gaussian function G(x) to {α_i”}_i=1^L, the resulting Gaussian-modulated attention array is denoted as {α_i”'}_i=1^L.
Subsequently, we identify the words from set {w_i}_i=1^L that rank within the top τ L based on their attention scores {α_i”'}_i=1^L.

    
  * Perform a sort on {α_i”'}_i=1^L on descending order, which yields an ordered set of indices {j_1, j_2, …, j_L}.
    
  * Select corresponding words from {w_i}_i=1^L with index {j_1, j_2, …, j_τ L} ,which yields {w_i'}_i=1^τ L.
    
  * Reorganize the set of selected words {w_i'}_i=1^τ L to reflect their original sequential order within the context.

Although the selection process targets individual words, the application of Gaussian filtering often leads to the selection of contiguous words, thereby effectively forming phrases.


  §.§.§ Sentence Level Filtering

In addition to phrase-level filtering, sentence-level filtering is also implemented to preserve more comprehensive semantic information. Using the NLTK toolkit, we extract semantic units at the sentence level. Each sentence s_i, denoted as 
s_i={w_j}_j=k+1^k+l, is assigned an attention score based on the maximum score of the tokens it contains. Subsequently, mirroring the phrase-level filtering process, we prioritize incorporating sentences with higher attention scores into the selection set, while ensuring that the aggregate word count remains below τ L.



  §.§.§ Dynamic Sentence Level Filtering

Sentence-level filtering often leads to a compression ratio greater than the designated target 1/τ. To more effectively adhere to the predetermined compression rate and optimize budget utilization, we augment the results of sentence-level filtering with word-level filtering. Specifically, subsequent to sentence-level filtering, if the count of words is L', we are then able to select an additional τ L-L' words. These additional words are chosen via phrase-level filtering from the text that was not previously selected. The newly selected words are subsequently concatenated with the results from sentence-level filtering to form the final compressed output.



§ EXPERIMENTS




 §.§ Datasets and Evaluation Metrics

In this paper, we assess the efficacy of the proposed QUITO method across two distinct scenarios: open domain question answering and long-form question answering. Specifically, we employ the NaturalQuestions (NQ) and ASQA datasets as the testbed.


For NQ dataset, We employed a processed version as described in <cit.>, where each query is paired with 20 documents, among which only one document contains the correct answer. In alignment with the procedures specified in <cit.>, accuracy was used as the metric to determine whether the generated responses accurately included the correct answer.
For the ASQA dataset, the answer to the question maybe multi-facet as there are many ambiguous questions.


Each ambiguous question in the ASQA dataset has answers reflecting multiple interpretations of these ambiguities. We utilize the dataset version provided by <cit.>, which includes 5 retrieved documents/snippets from Wikipedia for each query.
In accordance with <cit.>, our evaluation metrics included Exact Match (EM), a RoBERTa-based QA score (DisambigF1), and ROUGE <cit.>.







 §.§ Baselines and Implementation



  §.§.§ Baselines

We take three state-of-the-art compression approaches as baselines: For query-unaware methods, we select Selective-Context<cit.> and LLMLingua<cit.>, which implements cross entropy scoring to remove redundant vocabulary. For query-aware method, we compare our approach with Longllmlingua<cit.>. LongLLMLingua implements a two-stage compression method. It first evaluates and reranks multiple retrieved contexts, followed by a token-level compression stage, allocating varying compression budgets to these contexts based on their initial scores. 
For fair comparison, we excluded the context reranking phrase of LongLLMLingua (marked as LongLLMLingua† in Table <ref> and Figure <ref>), concentrating on the token-level compression.










  §.§.§ Detailed Implementation

For fair comparison, we follow LLMLingua <cit.> to use Longchat-13B-16k [https://huggingface.co/lmsys/longchat-13b-16k] as the generation model.

To ensure the reproducibility of the results, we apply greedy decoding strategy throughout the inference process, with the temperature parameter set to zero. The compression model is implemented with Qwen2-0.5B-Instruct[https://huggingface.co/Qwen/Qwen2-0.5B-Instruct].





 §.§ Main Results


Table <ref> presents the comparative performance of our method, QUITO, against three baseline methods across various compression rates and datasets. 
Firstly, we can see that selective-context is a strong baseline compared with LLMLingua and LongLLMLingua† on both 2x and 4x compression rates.
Secondly, QUITO obtains significantly better performances than all baselines, e.g., the improvement of QUITO with phrase level filtering against selective-context, LLMLingua, and LongLLMLingua†(i.e., 2x compression ratio) on NQ is 5.7, 20.2, and 17.7, respectively. 
Finally, we find that QUITO with different filtering method all achieve better performances on both datasets. However, there is no consistent advantages of each filtering method when compared on different datasets. This maybe that the context length on NQ and ASQA differs significantly, i.e., the average length of context on NQ and ASQA is about 2904 and 721 tokens, respectively.
All the results demonstrate the effectiveness of QUITO in compressing contexts for the LLM.









 §.§ Analysis on different position of the ground truth context

We analyse the performance of the QUITO compression method across different ground truth context positions within the NQ dataset. This dataset comprises 20 context document fragments per query, of which only one contains the answer and is designated as the ground truth document. We assessed the impact of this document's positioning at the 1st, 5th, 10th, 15th, and 20th ranks on the efficacy of various compression strategies. 






















axis background/.style=fill=white,
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The results presented in Figure <ref> indicate that all context compression methods struggle with the 'lost in the middle' phenomenon, as described by <cit.>. Performance is optimal when the ground truth context is positioned at the beginning; however, it deteriorates significantly when the ground truth context is placed in the middle. Among the evaluated methods, LLMLingua<cit.> exhibits the most resilience to the 'lost in the middle' phenomenon. This robustness may be attributed to its strategy of allocating higher compression ratios to contexts containing a greater density of information. Overall, the QUITO method consistently surpasses the two baseline methods across a variety of ground truth context positions and compression rates. On average, QUITO improves upon the performance of Selective Context<cit.> by +19.6 and LLMLingua<cit.> by +13.6.



 §.§ Analysis on different generation models






To better understand the generation ability of different LLM, we evaluate the performance of 4 widely-used models, including Longchat-13B-16k [https://huggingface.co/lmsys/longchat-13b-16k], Llama3-8b-Instruct [https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct], GLM4-9b-chat [https://huggingface.co/THUDM/glm-4-9b-chat], and Mistral-7b-instruct [https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2]. These models were tested with contexts compressed at a rate of 2 on the NQ dataset. The generated responses from these compressed contexts were then compared with those derived from uncompressed contexts.

As depicted in Figure <ref>, the Mistral-7B-Instruct model significantly outperforms the other three generation models despite having fewer parameters. This superior performance may be attributed to the incorporation of GQA and SWA during its training phase, which enhances its capability to process long sequence inputs.

While the context is compressed at 2x ratio, we find that the GLM4-9b-chat model show the smallest performance decline, with a decrease of 8.9, and the Mistral-7B-Instruct has the greatest decline.
When the compression ratio is 4x, we can see that all generation models obtain a relative close performance except for LongChat-13B-16k. These maybe that the LongChat-13B-16k is released earlier than other three models, and the latter are trained more deeply. 








§ CONCLUSION

This paper introduces the QUITO method, a novel attention-based importance estimation for long context compression in LLM.

The QUITO method employs a trigger token that comprehensively considers the query to assess the importance of each lexical unit within the context, thereby filtering out units with low relevance scores. 

Evaluations conducted on the NQ and ASQA datasets demonstrate that our method outperforms state-of-the-art compression methods such as Selective Context, LLMLingua, and LongLLMLingua, confirming its superior ability to preserve essential information needed by LLMs to respond to queries effectively.
For future work, we would like to study the combination of the context compression and re-ranking module, since the re-ranking stage in RAG also targets on selecting useful information for final answer generation. 










splncs04



























 Paper Title: MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning.



  
 
 

 
    
       
            
       
       
       
      
         












  














 



 

 

















            
       
    
       
       
      






 








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 Paper Title: HBot: A Chatbot for Healthcare Applications in Traditional Chinese Medicine Based on Human Body 3D Visualization.


















  Harbin Institute of Technology
  Harbin
  China

brolin@hit.edu.cn



  Harbin Institute of Technology
  Weihai
  China

22S030149@stu.hit.edu.cn



  Chinese Academy of Sciences
  Beijing
  China

jiahaowang0917@gmail.com



  Harbin Institute of Technology
  Weihai
  China

suidianbo@hit.edu.cn


Corresponding Author.

  Harbin Institute of Technology
  Weihai
  China

tzy_hit@hit.edu.cn


[1]

  Harbin Institute of Technology
  Weihai
  China

chudh@hit.edu.cn







§ ABSTRACT 

The unique diagnosis and treatment techniques and remarkable clinical efficacy of traditional Chinese medicine (TCM) make it play an important role in the field of elderly care and healthcare, especially in the rehabilitation of some common chronic diseases of the elderly. Therefore, building a TCM chatbot for healthcare application will help users obtain consultation services in a direct and natural way. However, concepts such as acupuncture points (acupoints) and meridians involved in TCM always appear in the consultation, which cannot be displayed intuitively. To this end, we develop a healthcare chatbot (HBot) based on a human body model in 3D and knowledge graph, which provides conversational services such as knowledge Q&A, prescription recommendation, moxibustion therapy recommendation, and acupoint search. When specific acupoints are involved in the conversations between user and HBot, the 3D body will jump to the corresponding acupoints and highlight them. Moreover, Hbot can also be used in training scenarios to accelerate the teaching process of TCM by intuitively displaying acupuncture points and knowledge cards. The demonstration video is available at <https://www.youtube.com/watch?v=UhQhutSKkTU>. Our code and dataset are publicly available at Gitee[<https://gitee.com/plabrolin/interactive-3d-acup.git>].




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   <concept>
       <concept_id>10002951.10003227.10010926</concept_id>
       <concept_desc>Information systems Computing platforms</concept_desc>
       <concept_significance>500</concept_significance>
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   <concept>
       <concept_id>10010147.10010178.10010179.10010182</concept_id>
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[500]Information systems Computing platforms
[500]Computing methodologies Natural language generation






HBot: A Chatbot for Healthcare Applications in Traditional Chinese Medicine Based on Human Body 3D Visualization
    Dianhui Chu
    Received 9th July 2024; accepted 13th July 2024
================================================================================================================





§ INTRODUCTION

Online medical dialogue systems have been playing an increasingly important role<cit.> in healthcare and medical care, with increasing adoption of such chatbots by patients, caregivers, and clinicians. Chatbots support a more direct and natural way of human-computer interaction via text-based or voice-based communication methods, which makes them suitable for a variety of target groups from young children to the elderly<cit.> and is an ideal candidate in the healthcare field for remote interventions on patients after discharge. To this end, the amount of medical dialogue systems is rising, and they cover a wide range of categories include mental health, physical health, health information, patient assistance, physician assistance, cognitive or developmental health<cit.>.

However, the TCM-related dialogue system has received less attention. Existing works mainly focuses on TCM diagnosis of diseases<cit.>, medical record management<cit.> and knowledge base construction<cit.>, while they do not have the ability to communicate with humans in natural language. Since the https://en.wikipedia.org/wiki/Acupunctureacupuncture points and meridians involved in TCM always appear in the consultation, we hope to tell users the clear location of these acupoints in an intuitive way. Therefore, we develop a healthcare chatbot (HBot) based on a human body model in 3D and knowledge graph, which provides conversational services such as knowledge Q&A, prescription recommendation, moxibustion therapy recommendation, and acupoint search. 

The user interface for this system is shown in Fig <ref>. The interface is divided into four parts: i)the top area is the navigation bar where users can search an acupoint visually, select registration or login page, and obtain API open capability. ii) the right area is the dashboard of 3D body where users can switch parts of the human body, zoom in&out and rotate the body, and click on specific acupoints iii) the left-top area is the dialogue frame where users can chat with HBot in text form and give instructions to operate the 3D body, and iv) the left-bottom area is the multimedia board where the pictures or videos related to acupuncture points and meridians will be displayed to help users understand the TCM-knowledge.



The main contributions are summarized as follows:

    
  * For the localization and display of acupoints involved in dialogues, we propose an interactive human body in 3D.
    
  * To support the consultation services, we construct a knowledge graph (TCM-KG) and annotate a document-level entity&relation extraction dataset in TCM.
    
  * Human evaluation results demonstrate the robustness of Hbot. Functions such as intent detection, entity&relation extraction, and knowledge query will be publicly released in our platform as open APIs.


 


§ SYSTEM ARCHITECTURE


The architecture of the HBot system is shown in Fig <ref>, where illustrates an example of interaction flow. HBot contains 6 key modules, Interactive 3D Body, User Intent Detection, Entity&Relation Extraction, LLM Handler, LLM Wrapper, and Knowledge Graph Construction. User Intent Detection module first classifies user queries and the slot filling task will be finished by the entity extraction module. To achieve multi-turns of dialogue, the history of mentioned entities and user intents will be be saved in a list for dialogue state tracking. According to the dialogue tracking history, the LLM Handler decides whether to trigger the module of Interactive 3D Body or retrieves from the knowledge graph (TCM-KG). Then, the execution result of LLM Handler will be fed into the LLM Wrapper to generate the response. Based on Knowledge Graph Construction, the triples obtained from Entity&Relation Extraction will be imported into Neo4j for the knowledge growth of TCM-KG. The module of Interactive 3D Body will be activated when user give an instruction or the acupoints are involved in the conversation. These modules are described in detail in the following sections.
 


§ INTERACTIVE 3D BODY

To visually display the meridians and acupoints of the human, we constructed an interactive 3D model of the human body. Considering compatibility of operating systems, simplicity of installation and diversity of interactions, we choose to embed our model into a web page and users can interact with the 3D body only through a web browser. Moreover, we pack the operations of the 3D body into javascript API for the developer to call. The implementation of the 3D body is the following steps:

1.According to the State Standard of the Location of Acupoints<cit.>, we manually make an adult male body and mark 409 acupoints by using 3D graphics software (https://www.blender.org/blender).

2.Using the GLTFLoader of https://threejs.org/docs/index.htmlthree.js, the 3D object files (.glb and .hdr) of the body will be loaded in the web page for real-time rendering and displaying.

3.Use the camera controller (controls.js) of three.js to implement 360-degree rotation of the model, switching, zooming and other operations. 

4.Using the event listener functions (onclick, onchange, onmouseover, etc) of Javascript, the switching of body components and the selection and restoration of acupuncture points can be realized. 





§ USER INTENT DETECTION

User Intent Detection is framed as a sentence classification task that aims to identify intents from human utterances, and plays the key role in dialogue systems<cit.>. However, training intent detection models relies upon a large set of user utterances paired with intents, which are predefined by the developers. In practice, the user will express new intents that may not be expected by the tested system, referred to as out-of-define (OOD) intents. To alleviate this issue, we propose a configurable intent detection process by applying three-phase: i)rule-based intent matching ii) model-based intent understanding and iii) similarity-based query searching. 

Phase I: an intent-table is maintained in the Redis database, where one predefined intent is mapped to 5 example utterances and the related regular expressions. We defined two types of intents: Body manipulation related (i.e. rotation, zoom in, zoom out) and dialog content related (i.e. asking about treatment, asking about symptoms). When Hbot is running, the user utterance will be matched to a certain intent based on the regular expressions. If the match fails, Hbot will enter the second phase: model-based intent understanding.


Phase II: we applied the complex intent detection model Conco-ERNIE<cit.>, which is effective for both multiple intents and implicit intent detection by exploiting co-occurrence patterns between concepts appeared in medical queries and user intents conveyed by these queries. Given a user query, Conco-ERNIE can predict the probability distribution of the utterance on 21 common medical intents. If the probability of each intent is less than the threshold (0.6), Hbot will enter the third phase: similarity-based query searching.

Phase III: We collected a real question-and-answer corpus between patients and doctors (about 100k Q&A pairs) from medical online forum[<http://club.xywy.com/list_score.htm>]. To ensure the quality of the corpus, we only select the Q&A pairs where the question has a bounty and the answer is adopted by the patient. To find the approximate answers, we trained a sentence embedding model SBERT<cit.> with siamese network architecture to calculate the similarity between the input sentence and each query in the corpus. Then, the answer to the query most similar to the input sentence will be returned to the user. Followed by <cit.>, the positive samples of the input question are constructed through word repetition operation. Since entities(i.e. disease, symptoms) appeared in the medical queries have a great influence on sentence semantics, we construct negative samples by randomly replacing the entities of the same or different types.



§ ENTITY&RELATION EXTRACTION
 


The task of entity extraction (similar to slot filling) is to identify entities in text with their corresponding type, which is a critical component in spoken dialogue systems<cit.>. BERT<cit.> with a CRF layer have been widely used in named entity recognition and achieved good performance. So we trained a BERT-CRF model to identify all all TCM-related entities in user utterances. 

The task of relation extraction is to identify the relationship between entities from text<cit.>, which is essential for the construction of TCM knowledge graph. Under the guidance of medical experts, we finely annotated a small-scale but high-quality document-level entity&relation extraction dataset (contains 270 documents, 5996 entities, 4685 relations) based on TCM books. Followed by <cit.>, we treat relation extraction as a multi-label text classification problem. Given an entity pair (head, tail) and the evidence sentences that used to judge its relation, the spliced text of entity pair will be encoded as e by BERT and the i-th evidence sentence will be encoded as s_i by the same BERT. All the representations of evidence sentences [s_1,s_2,..,s_k] will be fed into a Bi-GRU layer, then the final state vector h of Bi-GRU layer and e will be concatenated together and then use a softmax function to compute the probability for each relation type. The relation extraction model is shown as Fig <ref> and the performance is shown in Table <ref>.




 




§ LLM HANDLER AND WRAPPER

Based on the prompt engineering, the LLM Handler utilize the large language model to choose the next action for the given dialogue history. To respond to users' medical questions, the LLM handler retrieves from two types of data sources: documents and knowledge graphs, in order to obtain text-based answers and entity-based answers respectively. The dialogue history, current user intent, and these two types of answers are input into the LLM Wrapper to generate a fluent answer that conforms to the user instructions. Essentially, our LLM wrapper is a RAG (Retrieval-Augmented Generation) system that comprehensively utilizes knowledge from multiple sources and of multiple types. The LLM adapted by both of these modules is ChatGLM3-6B [<https://huggingface.co/THUDM/chatglm3-6b-base>].



§ KNOWLEDGE GRAPH CONSTRUCTION

Knowledge graph is a knowledge base that uses a graph-structured model to integrate data, which is the cornerstone of question answering system. To implement question answering in the domain of TCM healthcare, we construct a TCM-related knowledge graph through three steps: i) ontology modeling ii) instance injection and iii) knowledge growth.

Step I: Followed by ontology development 101 <cit.>, we create the ontology model in the domain of TCM.  

We first enumerated the important terms in TCM-ontology and defined their properties. Then we defined the relations between different terms.

After that, TCM-ontology contained 12 types of entities, 8 types of relations and 20 types of properties.

Step II: Since the entities and relations in the extraction dataset mentioned in Sec. <ref> are annotated according to TCM-ontology, it is feasible to restored the knowledge triples aligned with the ontology from the dataset directly, and then injected them into the graph database (Neo4j) after deduplication. Through instance injection, we construct a handcrafted TCM domain KG with 37851 triples. Fig <ref> shows a 3-hop subgraph of with facial paralysis (entity type is Disease) as the central node, where gray nodes represent symptoms, orange nodes represent human acupuncture points, purple nodes represent diseases and pink nodes represent moxibustion therapy. 

Step III: To obtain a larger-scale TCM-KG, we crawled more documents from the National service http://www.gjmlzy.com:83/platform for academic experience of famous TCM doctors, and used the entity&relation extraction model mentioned in Sec.<ref> to find more triples from these documents. After manual proofreading, the correct triples are injected into Neo4j. The size of TCM-KG grows from 37851 triples to 48633 triples.









§ HUMAN EVALUATION

Followed by the methodologies of software testing <cit.>, we conducted https://www.guru99.com/alpha-beta-testing-demystified.htmlα&β testing against user requirements to determine whether HBot satisfies the acceptance criteria. α-testing performed to identify all possible issues and bugs before releasing the final product to the end users. A total of 5 members in the development team conducted α-testing on HBot from three aspects: the feedback on 3D model operation, the accuracy of user instruction understanding, and the robustness of system at runtime. During α-testing, we executed 100 test cases and found 21 bugs, including 10 error feedbacks, 8 responses that did not satisfy instructions, and 3 situations where the system could not run stably. After fixing these bugs, we hired 8 members of our lab to conduct β-testing on HBot. These people were only informed of the system's features and did not grasp the development details, who can be regarded as end users. During β-testing, each member was asked to give 15 different instructions to HBot in the form of text in the dialog box. Each instruction is recorded along with the corresponding feedback or response from HBot and the evaluation of user satisfaction. User satisfaction measures whether the feedback or response of HBot meets the expectations of users:

    
  * Bad: not at all as expected 
    
  * Fair: as expected, with tolerable errors
    
  * Good: exactly as expected
    
  * None: no feedback or response was returned

The performance of HBot on α and β testing is shown in Fig <ref>. After a round of bug fixes, the responses of HBot are generally more in line with user expectations. However, there are more cases where the user evaluation is none. This is because the testers are developers in α-testing and they know where the capability boundaries of the system are, while the testers in β-testing often give instructions that are outside the scope of the domain (e.g. chat with them, amuse them and ask how much is the surgery). To continuously improve system performance, we developed the evaluation interfaces for end-user and the function of record collection. When the scale of the records is large enough, relevant modules in HBot will be updated offline by analyzing these records.






§ CONCLUSIONS

To provide TCM-related conversational services such as knowledge Q&A, prescription recommendation, moxibustion therapy recommendation, and acupoint search, we develop a healthcare chatbot (HBot). Based on an interactive human body in 3D, HBot supports the localization and display of acupoints in conversations. Moreover, we annotate an entity&relation extraction dataset and construct a knowledge graph in TCM to support the slot filling and knowledge retrieval functions of HBot.


This work is supported by the National Key R&D Program of China (2022YFF0903100), the Special Funding Program of Shandong Taishan Scholars Project, the Key projects of Shandong Natural Science Foundation (ZR2020KF019) and Harbin Institute of Technology Graduate Teaching Reform Project (23Z-DZ039).
















 Paper Title: Bug Analysis Towards Bug Resolution Time Prediction.












Bug Analysis Towards Bug Resolution Time Prediction
    

Hasan Yagiz Özkan1,
Poul E. Heegaard2,
Wolfgang Kellerer1,
Carmen Mas-Machuca3,



1Technical University of Munich, Chair of Communication Networks, Germany 

2NTNU - Norwegian University of Science and Technology, Department of Information Security and Communication Technology,



3 University of the Bundeswehr Munich, Chair of Communication Networks, Germany



    August 5, 2024
===================================================================================================================================================================================================================================================================================================================================================================================



§ ABSTRACT 

Bugs are inevitable in software development, and their reporting in open repositories can enhance software transparency and reliability assessment. This study aims to extract information from the issue tracking system Jira and proposes a methodology to estimate resolution time for new bugs. The methodology is applied to network project ONAP, addressing concerns of network operators and manufacturers. This research provides insights into bug resolution times and related aspects in network softwarization projects.







 Data Mining, Bug Fixing Time, Softwarized Networks, Machine Learning







§ INTRODUCTION





Network softwarization entails replacing dedicated hardware for specific applications with programmable components, leading to advantages like easy maintenance, flexibility, scalability and decreased costs associated with development and operations<cit.>. Software Defined Networking (SDN) and Network Function Virtualization (NFV) are examples of these softwarization efforts. In this context, manufacturers rely on open-source solutions to provide extra functionalities while complying with the standards.

However, despite the advantages of network softwarization, operators are reluctant to use these solutions as the software is more complexity, incurring to more bugs, as well as the code reliability is uncertain as it is open to any software developer(s) differing in terms of programming skills, experience, trustiness, etc.   


In order to increase the transparency of software reliability, any bugs which are encountered during the operation of a software, are reported in open repositories. These repositories allow knowing the reported bugs, their status, etc., which are useful to evaluate the software reliability. Each software project has defined a bug resolution process, which should be followed by any software developer (i.e., debugger) of that project. 

The goal of this study is first of all, give an overview of the information that can be extracted from these repositories. Some information can be directly obtained from the gathered data, but some other need some processing. Secondly, we propose a methodology that can be used to estimate the resolution time of new bugs. The proposed methodology has been applied to several network related projects such as ONOS, ONAP, etc. We believe that network operators and manufacturers using an open source project like those, are concerned about the bug resolution times and other aspects addressed in this paper.  







The contributions of this paper are:

    
  * Collection strategy of reported bugs and the required filtering.
    
  * Basic data analysis such as identification of the most common paths in the workflow, the transition distribution between different states, bug resolution time distribution, etc.
    
  * Advance data analysis e.g., impact of aspects such as priority, reporter to the debugging time. 

    
  * Investigating state probabilities of the bugs in time.
    
  * Comparison of the bug resolution time prediction with different strategies
    
  * Predicting the exact bug resolution time for different bugs with neural network.

The paper is organized as follows: Section II gives an overview of the state of the art on bug data analysis and bug resolution time models. Section III introduces the bug repositories and the data that can be extracted from them. Section IV presents the proposed bug collection and the data analysis used in this work. Section V proposes a machine learning model to estimate the bug resolution time of new bugs. Finally, Section VI concludes the paper.













§ RELATED WORK



This section will discuss previous works on bug data analysis and bug resolution processes. 

Issue tracking systems are mined, and researchers of <cit.> use this process to offer the dataset of all the issues from different projects. Additionally, the authors of <cit.> conduct various analyses of the collected data, which comes from 55 different Apache projects. These 55 projects are classified into 9 categories, such as big data, security, and libraries. The authors examine the percentage of fields that change the reporting of the bugs. They define bug resolution duration time from the creation and closure of a bug as a bug-fix effort and compare the bug resolution times across different project categories. Additionally, they investigate when the priority and assignee are defined and analyze the number of bugs in which these values change after the initial bug report. Furthermore, the study delves into the changes in bug status, analyzing the percentage of bugs transitioning between states. This dataset is used in this work. As the primary contribution of this work is the provided dataset, it does not delve into a comprehensive analysis or propose a methodology to estimate bug resolution duration. In addition to the self-collected data, the bug reports from <cit.> are also examined in this study.

The authors investigate the impact of bug attributes such as reporter and priority on the time it takes to resolve them in the study <cit.>. The used data in this work is not publicly accessible. They employed the K-nearest neighbour algorithm to predict the resolution time of bugs. Based on these attributes from the issue-tracking system, they predicted whether a bug would be resolved quickly or slowly. The authors of <cit.> replicated the study presented in <cit.>, but they utilized open-source data sourced from Bugzilla Firefox. Both studies concluded that the identity of the reporter or assignee is more crucial than the bug's priority. However, a weakness of these predictions is that they are binary (quick or slow) rather than numerical. 

Furthermore, their proposal incorporates a Markov-based approach to estimate the number of bugs that can be resolved within a specific timeframe. They also employ a Monte Carlo-based method to predict the overall time required to fix a given quantity of bugs. The time is divided into months, and they estimate the percentage of bugs that will be resolved within a particular time period. It is important to note that the Markov-based model they used does not focus on examining the solution process of individual bugs as separate entities but rather on determining the percentage of bugs in different states after a certain period of time, measured in months.
























§ DATA REPOSITORIES




Issue tracking systems are software tools used to manage software projects' development, maintenance and debugging processes <cit.>. These systems use issues which can describe bugs, feature requests, milestones and any other tasks, depending on the requirements of the considered project. Moreover, issue tracking systems also serve as a repository for the history of reported and addressed issues. Thus, they offer an excellent opportunity to understand the software project's development and debugging process. Most open-source software use publicly available issue-tracking systems because it facilitates collaboration among software developers and debuggers by giving access to the status, process, and resolution of the reported issues.

Jira is one of the most popular issue-tracking systems used by many different open-source projects<cit.>. As an issue tracking system, the issue types in Jira extend beyond 'Bug,' including 'Task,' 'Story,' and more. Every issue is kept in the Jira repository of the respective project. Therefore, Jira repositories become a valuable information source for the project's development history. Jira defines a workflow, which comprises of a sequence of stages that issues may undergo, from their creation (referred as "Open") and ending with their resolution (referred as "Closed"). The workflow is depicted in Fig. <ref> in blue color. 
Although Jira provides this initial workflow, it can be customized to meet the specific requirements of a project. 

This study considers two open-source projects as examples: Open Network Operation System (ONOS) and Open Network Automation Platform (ONAP). ONOS, developed by the Open Network Foundation, is an operating system designed for software-defined networking (SDN) and follows a standard workflow. ONAP is a Network Function Virtualization (NFV) Management and Orchestration (MANO) framework developed by the Linux Network Foundation <cit.>. ONAP involves collaboration among various working groups. ONAP community modified the standard workflow of Atlassian to fit their bug-solving strategy by adding a state referred as "Submitted". Standard Atlassian workflow and modifications of ONAP is shown in Fig. <ref>.

Furthermore, the dataset provided by <cit.> is also analized. This dataset includes bug reports from 55 Apache projects. These Apache projects also use a modified version of the standard Atlassian workflow, which can be seen in Fig. <ref>. This modified workflow has an additional state called 'Patch Available', similar to the 'Submitted' state of ONAP.



















 §.§ Bug related data



In this work, we are interested in the bug resolution time and hence, all issues other than 'Bug' are filtered out. Jira provides a collection of reported bugs. This collection contains details about the bug and its resolution process. Certain information is provided when a bug is reported, while others change throughout the resolution process. The following information is utilized in our analysis:


    
  * Priority indicates the importance of the bug. The priority levels vary between projects. Hence, in this work, priorities 1 and 2 refer to the highest and second highest priority of any project.
    
  * Reporter is the person reporting the bug.  
    
  * Assignee is the person who is responsible for fixing the bug.
    
  * Creation Time is assigned to the date and time when the bug was reported.
    
  * State corresponds to one of the different states of the workflow. The initial state when reporting a new bug is "Open". The state is updated during the resolution process, according to the state in the workflow.
    
  * Resolution Status reflects the progression of the resolution process, initially set as "Unresolved" and subsequently updated based on outcomes such as "Fixed" or "Cannot Reproduce" upon completion of bug resolution.
    
  * Update Time is the time of the last edit
    
  * State Transition includes the previous and next states and the time of the transition.




Additional attributes that can be found in Jira are listed below:


    
  * Summary is the brief description of the bug.
    
  * Sub-Project is the sub-project to which the bug is associated.
    
  * Description is the detailed explanation of the bug.
    
  * Labels  are issue-related labels.
    
  * Affects Version is the version of the project in which the issue is occurring.
    
  * Fix Version is the version of the project in which the issue was resolved.
    
  * Watches Count is the number of people watching the issue.
    
  * Comments are the comments during the bug resolution process.

    




Any modification to bug-related information in Jira (such as status, states, or additional comments) is collected, along with the timestamp and the person responsible for the update. This allows us to determine the number of people involved in the resolution process or the calculation of the time between commits and state transitions. The collected state transitions are employed to calculate the duration in each state until the bug is resolved.
















 §.§ Bug resolution process
 

As bugs in Jira are a specific type of issue, they also follow the workflow for the issues. While other transitions from Fig. <ref> are also utilized, a primary path within the workflow is typically followed for the bug resolution process. A bug begins in the 'Open' state when it is initially reported. Once a developer starts working on the bug resolution, the state is changed to 'In Progress'. The state of the bug changes to the 'Resolved' state, once the bug is resolved, where it awaits approval by another team member. After approval, the bug is closed by moving to the 'Closed' state. However, a bug may reappear so that it can be reopened and returned to the 'Reopen' state.


 


§ BUG COLLECTION AND ANALYSIS



A developed Python script has been deployed for the proposed bug collection and analysis. It is important to mention that the bugs for ONAP and ONOS were collected directly from Jira. However, the bugs for Apache were collected from the dataset at <cit.>. The data analysis included in this work focuses on the two top-priority bugs (i.e., priority 1 and 2).



 §.§ Bug collection and filtering





The collected data in this study includes the reported bugs from the start of each project to the collection date (in this case, 05.04.2023). The collected data encompasses all bug related details (refer to Section <ref>) as well as all related information during the bug resolution process such as transition states and times, authors involved in each change, etc.


In this study, we investigate the bug resolution process. However, the dataset does not only include the data relevant to our study, but it also includes exceptional/unwilling data, which must be filtered.


Let us introduce the proposed data filtering on the dataset:

    
  * Not Resolved Bugs: 
   Not all reported bugs are necessarily resolved; some of them may be closed for alternative reasons. The resolution status is determined as a result of the debugging process. In the case of ONAP and ONOS bugs, the resolution status begins as 'Unresolved' and subsequently changes to one of the multiple resolution statuses, which are listed in Table <ref>. The table also presents the respective percentages of each status. As this study targets the resolution time of bugs, only the bugs with the status 'Done' and 'Fixed' are considered, which account for more than 80% of the priority 1 and 2 bugs for both ONAP and ONOS projects. Regarding the Apache dataset, no filtering is necessary as the considered dataset contains only bugs with a resolution status of 'Fixed'.
    
  * Transient State: As depicted in Fig. <ref>, the resolution process consists of a set of transitions between different states, starting at "Open" and ending at "Closed". For the bug resolution time study, it is relevant to know the time at each state before any transition. However, in certain cases, bugs may spend only a few minutes or even seconds in a particular state. This short duration might not provide accurate information about the actual effort expended in that state. Transient states of this nature can arise due to automated tasks or the practice of updating Jira after completing multiple states. Consequently, states with a duration shorter than a few minutes 
    are removed as a state, and the time is added to the original state. 
    
  * Not Defined States: Observations of the dataset revealed that a few bugs have states that are not defined within the workflow of the project. The number of such states is significantly lower than those within the Jira workflow. Since the states are not in the workflow, we filter them out. The duration spent in these states is accumulated with the previous state, ensuring that the resolution duration remains unaffected.
    
  * Loop to the same State: There is a possibility that a bug has a transition from a state to the same state (referred as loop). Some of these transitions are defined as such in Jira, while others may arise due to the removal of transient states or undefined states. In the case of a loop, no additional transition is considered, and the time spent in these states is merged. 
    
  * Transitions from "Close" State: The goal of finding the bug resolution time implies that bugs eventually reach the "Closed" state. While some bugs may occasionally be reopened after a certain period, it is often observed that reopening occurs after a substantial amount of time, which significantly inflates the resolution time. To address this issue, the "Closed" state is treated as an absorbing state, meaning that once a bug reaches this state, any subsequent transitions from the "Closed" state are excluded.




 

















 §.§ Bug resolution time distribution and common flows in transition workflow






The resolution time is defined as the time elapsed from the reporting of a bug (i.e., in the "Open" state) to the time when it reaches the "Closed" state. The resolution time is the sum of the time spent on various states, but it does not provide information about the duration spent on each individual state.


In this section, we introduce the resolution time analysis for the particular case of the priority 1 bugs of the ONAP project.  These priority 1 bugs amount to a total of 906 in the project. 
The most common resolution paths for all the ONAP priority 1 bugs, can be found, as summarized in Table <ref>.

It can be observed in this case, that almost 91% of the bugs follow the seven main paths. Furthermore, 45% of the bugs follow the path  Open-In Progress-Closed or  Open-Closed.











The time spent at each state before any possible transition can also be found. These time distributions are crucial for bug resolution time estimation from a specific state to the 'Closed' state. For this purpose, Table <ref> provides the mean and median time spent in a state before a particular transition to a different state, as well as the number of such transitions. 
Upon close examination of the transitions from the 'Open' state, it becomes apparent that the duration is longer when a bug transitions directly to the 'Closed' or 'Delivered' states compared to transitioning to the 'In Progress' or 'Submitted' states. One possible explanation for this phenomenon is that bugs typically progress through these states, even though they may not be explicitly recorded in Jira. 

The significant difference between the mean and median time for most of the states is primarily attributed to the fact that most of the bugs have a relatively short duration in each state. However, some bugs are more challenging to handle or the assignee starts later to  work on them, which causes outliers and significantly increases the mean values.






 §.§ Impact of different bug aspects



This section investigates the impact of different properties of the bugs on the bug resolution time. Properties like priority level, assignee and reporter of a particular bug may impact its resolution duration. The Apache dataset encompasses various projects, but we have excluded this data to eliminate variations caused by project differences. The number of bugs in ONOS should be higher to ensure a meaningful comparison. Thus, ONAP is the preferred option for investigating the assignee's and reporter's influence. Similar to the previous case, the bugs are separated based on priority.


Let us first analyse the impact of the reporter on the bug resolution time. The reporter of the bug may impact the time given his/her experience by providing an accurate and clear bug description as well as listing all the essential information to reproduce the bug accurately.






Fig. <ref> depicts the impact of the top 10 reporters who reported the highest number of bugs, on the bug resolution time for priorities 1 and 2. The ordering of reporters is determined by the median value of the priority 2 bugs, taking into consideration that some reporters have a limited number of priority 1 bugs, with half of them reporting fewer than 20 bugs. The total number of reported bugs is shown under the x-axis of the graph, whereas the median resolution time for each case is given at the top of the graph. The decision to use the median value instead of the mean value is made in order to prioritize typical values over outliers, as the mean value can be heavily influenced by the outliers. Firstly, it is noteworthy that the resolution time for priority 1 bugs is generally shorter than that of priority 2 bugs for most reporters. Secondly, the impact of reporters on the bug resolution process is significant. In comparison to the reporter with the slowest bug resolution times, the one with the fastest experiences a median value that is 15 times quicker for priority 1 bugs and 35 times quicker for priority 2 bugs.




Another important factor is the assignee of a bug, who is responsible for resolving it. Assignees possess different expertise, availability, and familiarity with the project. The impact of various assignees is presented in Fig. <ref>. Similar to reporters, the top 10 assignees, assigned to the most bugs, are selected and ordered based on the median values of priority 2 bugs. It is evident that the assignee significantly affects the bug resolution time. Moreover, the median values on top of the figure indicate that nine out of ten authors tend to resolve higher priority bugs faster.

















The previous observations in this section indicate that the reporter and assignee play an important role in the bug resolution process. However, the reporter and assignee can be the same person, which may occur when the reporter wants to take responsibility for its resolution, e.g. when the reporter is familiar with that part of the code. In this case, it can be assumed that reproducing the bug can be easier, and there is no time required to communicate the assignee with the reporter to comprehend the bug. Thus, we further explore the impact of assigning the reporter to him/herself than to a different assignee. Fig. <ref> shows that assigning the reporter to the issue has a significant impact on bug solving process for both priority 1 and 2. 



 





 §.§ Most relevant workflow paths and study of state times









 §.§ Bug Distribution across States over Time



In this section, the distribution of bug states over time is presented. It presents the probabilities of being in specific states at given times for the ONAP, ONOS, and Apache projects.


The time when a bug is reported (i.e., the bug is at 'Open' state), is considered to be the time 0. Hence, at this time, the initial percentage of bugs in the 'Open' state is 100% (1.0) and zero at the other states. As time progresses, the bugs change to different states, leading to different distributions across the various states. The percentage of bugs in different states over time is provided for ONAP, ONOS and Apache projects in Fig. <ref> for priorities 1 and 2. In all cases, it can be easily observed that since the 'Closed' state is absorbing, there is an always increasing percentage of bugs in that state. 

The analysis of Fig. <ref> reveals a notable distinction in bug resolution times among different projects. Specifically, in ONAP, bugs consistently tend to reach the 'Closed' state within approximately 8,000 hours, independently of the priority. Conversely, such a trend is not observed for bugs in ONOS and Apache projects, as after this time, still 10-20% of the bugs have not reached the "Closed" state. This divergence stems from the approach adopted by Apache projects, where the emphasis is not solely on expeditiously closing bugs, but rather on considering them solved once they reach the 'Resolved' state. Additionally, in the ONOS project, some bugs remained in the 'Delivered' state for an extended period before eventually being closed.
 







Examining the percentage of bugs that achieve 'Closed' status, it can be observed that for priority 1 bugs, 50% is reached after around 1,200 hours, while for priority 2 bugs, it takes approximately 2,000 hours. Comparatively, in ONAP and ONOS projects, more than half of the bugs are closed within 300 hours for priority 1 bugs and within 500 hours for priority 2 bugs. Analyzing the graph depicting bugs in the 'Closed' state, it becomes apparent that the curve for ONAP bugs exhibits a steeper ascent compared to that of ONOS and Apache projects. Additionally, it is noticeable that the number of bugs in the 'Open' state decreases more rapidly in the ONAP project. This observation suggests that the debugging teams in ONAP work on the bugs at an earlier stage in comparison to the other projects. 

However, it is important to note that making a direct comparison is challenging due to variations in the strategies employed by different working groups when transitioning between states in their respective workflows. Some projects may strictly update the bug's state as soon as it is reached in the actual handling process, while others may implement such changes after a certain amount of time has passed. This discrepancy becomes visible in the utilization of the 'Delivered' state in Fig. <ref>. In Apache projects, bugs in the 'Delivered' state can be considered solved, whereas the same claim may not hold true for ONAP and ONOS projects.






























 
 
 
 
 

































§ PREDICTING RESOLUTION TIME WITH NEURAL NETWORK



Even after doing filterings, which are explained in Section <ref>, there are still bugs that can be filtered. One of the main reason to filter is the long living bugs, which are discussed in Subsection <ref>. Possible reasons of the short and long living bugs and different possibilities to filtering those bugs are discussed in <cit.>. It has been explained that some bugs are forgotten, wrongly labeled or reported after its resolution. Therefore removing short living bugs or long living bugs might improve prediction quality. In results we compared different filtering strategies for the bugs. In <cit.>, authors discuss removing long living bugs with the help of inner fence and outer fence. We also used the same strategy if all the bugs outside of inner fence is removed it is called removing mild outliers in case of all bugs above outer fence is removed is called removing extreme outliers. As an extend to these outlier removal we also proposed to remove the bugs without any update (comment, state change etc.) for more than 30 days. We compared these filtering methods and investigate their impact on the results.

We used neural networks (NN) to predict the resolution time of the bugs. The results are compared with Naive Bayesian (NB) and k Nearest Neighbour (kNN) algorithms. These two algorithms are chosen because they are widely used for resolution time prediction <cit.>. Since the NB and kNN cannot be applied to get numerical results but only used for classification, the results which used for comparison takes two classes into account. These two classes divided depending on the resolution time. If the resolution time is shorter than median resolution time of the training dataset, the bug considered as 'fast' bug otherwise it is classed as 'slow' bug. 

The input of the NN is the information related to the priority, subproject, assignee, reporter and either the assignee is same person of the reporter or not. Two different NN model compared for the classification one of them is trained for binary classification and returns either the bug is belongs to 'fast' or 'slow' group and the other one predicts the exact resolution time. The size and input of those models are same, where the difference lies on the loss, activation functions and the output values. For binary predicted output values are either 0 or 1 and for exact value prediction values are time to solve as hours. Since the exact time prediction is not same as binary classification, the time prediction values should be converted to two class depending on either it is predicted lower than median time or higher than median time to resolve. Both two NN consist of 5 layers, where the first one is dedicated to normalization of the input data. 

The ONAP project is selected for the model training, because it has more bug reports than the ONOS project and insufficient amount of data might impact the performance of the NN. In order to make prediction we divided the dataset into two group, training, which corresponds to 80% of all dataset and test which is the rest of the dataset. In order to be able to generalize the results we cross validate our results with randomly dividing the dataset 10 times. As it can be seen from the Fig.<ref>, even though NN does not make binary estimation of 'slow' and 'fast' its accuracy is higher than kNN estimation for any type of filtering. NN results for binary classification provides either best or second best results in any case. The filtering does not help to improve the results, one reason for that can be the reduction of the training dataset. Another possible reason is the filtered bugs mostly have similar information like assignee or subproject and classifying them as 'slow' increase the accuracy. 


The results for exact time prediction is given in Figure <ref>. It can be seen that the median normalized prediction error is 20% for all bugs and 30% for filtered bugs. One of the reason for this high error can be the holiday periods and weekends. The bug report day actually plays an important role on its resolution time. Even though there is an error around 20% between predicted and real time to solve, predicted value can be more helpful than two classes.














§ CONCLUSION


In this work the importance of different aspects of the bugs on their resolution process has been shown. The time to solve a bug decrease, when the reporter is assigned to the issue. Also the impact of different resolution process or strategy to report this process has been shown. It has been seen that in Apache projects, reaching to the 'Closed' state is not forced like ONAP, instead the bugs stays in 'Resolved' state for long time. It has been shown that bug resolution time can be estimated with neural networks and the predicted value can be used by the developer teams to distribute their workforce according to the estimation. This work can be extended with using different machine learning techniques like pretraining and data augmentation to make a valid prediction for new and smaller projects.




§ ACKNOWLEDGMENT


This work has received funding from the "DFG" under grant numbers MA 6529/4-1 and KE 1863/10-1.









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 Paper Title: Moderating Group Conversation Dynamics with Social Robots.



















































Moderating Group Conversation Dynamics
with Social Robots
    Lucrezia Grassi^1, Carmine Tommaso Recchiuto^1, Antonio Sgorbissa^1
^1All authors are with the University of Genoa, DIBRIS, Via All'Opera Pia 13, 16145, Genoa, Italy.

Corresponding author's email: 



    August 5, 2024
=============================================================================================================================================================================================================



§ ABSTRACT 
This research investigates the impact of social robot participation in group conversations and assesses the effectiveness of various addressing policies. The study involved 300 participants, divided into groups of four, interacting with a humanoid robot serving as the moderator. The robot utilized conversation data to determine the most appropriate speaker to address. The findings indicate that the robot's addressing policy significantly influenced conversation dynamics, resulting in more balanced attention to each participant and a reduction in subgroup formation.






§ INTRODUCTION

Social robotics focuses on creating and applying robots designed to engage with humans in social environments. The use of social robots in group interactions is attracting increasing interest due to the growing need for these robots to engage with multiple users simultaneously. This includes scenarios such as robots participating in discussions with several individuals <cit.>, facilitating social interactions <cit.>, and engaging in social games <cit.>.

Robots capable of engaging with multiple people can enhance user experiences by making interactions more compelling and natural. This can lead to higher user satisfaction and improved accessibility and inclusivity, as highlighted by <cit.>. However, at present, few robots can engage with multiple users simultaneously. This is due to the many factors that need to be considered in these situations. A system described in <cit.> examines the tracking and fusion aspects of multi-party interactions but only monitors user entry and exit and can accurately identify only two users. A spoken dialogue system like the one in <cit.> can identify multiple users using data from a Kinect sensor, but its conversational abilities are limited, and it struggles with long, natural conversations involving multiple parties. Additionally, it can only engage one person at a time. Similar limitations are observed in the work of <cit.>, which aims to develop a multi-user engagement policy for managing turn-taking using the robot's gaze, head movements, and verbal communication. 

Whenever the robot interacts with multiple people, the “many minds problem” arises <cit.>. As the number of participants increases, basic conversational mechanisms such as turn-taking, speaking time, and listener feedback become more complex. Although turn-taking is a fundamental aspect of communication, researchers continue to study how speakers signal the end of their turn and indicate who they are addressing, as well as how listeners recognize when it is their turn to speak. This is accomplished through behaviors such as gaze, head orientation, and intonation, which have been studied by various scholars, including <cit.>. Speakers typically use gaze to select the next speaker, known as the “addressee” <cit.>. The selection of the addressee is a crucial issue in group conversations, requiring speakers to make quick decisions that consider the potential reactions of other participants.



Furthermore, the robot needs to comprehend the participation levels of the users it is interacting with. Dominance, a key concept in social interactions, plays a significant role in this understanding and has been extensively studied in social psychology <cit.>. Dominance can pertain to an individual's traits or their hierarchical status within a group. Indicators of dominance are categorized into vocalic and kinesic types. Vocalic indicators include factors such as speaking time, word count, and speech loudness (or energy) <cit.>. Speaking activity, especially the duration of speech, is a strong predictor of dominance. Kinesic indicators involve body movement, posture, facial expressions, and eye gaze <cit.>. Dominant individuals generally exhibit more movement and a broader range of motion than non-dominant individuals, and their gestures during speech are positively associated with dominance <cit.>.



This paper presents a study that aims to investigate and regulate the dynamics of group conversations involving a social robot. In such interactions, participants may have varying roles and personalities, leading some individuals to dominate the conversation while others may feel excluded. Ensuring balanced participation is essential to make everyone feel included and engaged.

To manage the dynamics of the conversation, particularly when it is crucial for all participants to feel like part of a cohesive group, we implemented four different control policies that leverage the concepts of dominance and communities. To validate these policies, we tested them against a baseline policy. We conducted 75 experiments involving a total of 300 participants, where a humanoid robot engaged in conversations with groups of four individuals. Figure <ref> depicts the robot interacting with the participants in one of the experiments. Throughout these experiments, the robot collected quantitative data to analyze participation levels, identify distinct subgroups (i.e., communities) of participants, and evaluate the overall conversation dynamics. This information allows the robot to act as a moderator, promoting active participation in the conversation.

The article is structured as follows. Section <ref> provides an overview of the system architecture and the developed control policies. Section <ref> describes the experimental setup and discusses the findings. Finally, Section <ref> presents the conclusions.



§ METHODOLOGY




 §.§ System Architecture

The robot's ability to converse autonomously with multiple people is enabled by CAIR (Cloud Artificial Intelligence and Robotics), a cloud software architecture specifically designed for autonomous conversation <cit.>. This system relies on a framework for knowledge representation, utilizing an ontology implemented in OWL2. Conversation topics and related sentence pieces are dynamically composed at runtime using the hierarchical structure of the knowledge base.

The CAIR server comprises a set of web services, as illustrated in Figure <ref>. The Dialogue Manager service manages the conversation and identifies the user's intent to discuss specific topics, while the Plan Manager service interprets the user's intent to direct the robot to perform particular actions. To generate appropriate responses and plans, the server uses an Ontology that includes all the topics, keywords, sentences, and plans used during interactions with users <cit.>. An additional service called Hub handles all incoming requests by forwarding them to the Dialogue Manager and Plan Manager services. Information exchange between the CAIR client and server is facilitated through the dialogue state, which tracks the conversation's history and includes discussed topics, preferred topics based on user input, and previously spoken sentences to avoid repetition.

To enable multi-party interaction, the original architecture described in <cit.> has been enhanced with two new services: the Registration service and the Audio Recorder service. The Registration service is activated when a new user initiates registration, creating a new profile ID linked to the user's voice. The Audio Recorder service starts recording audio when the Root Mean Square (RMS) of the noise exceeds a specific threshold, sending the audio to the Speech Recognition API for transcription and to the Speaker Recognition API to identify the speaker's profile ID.




The dialogue state has been expanded to support multi-party interaction, incorporating data related to the speakers along with dialogue statistics. These include a matrix tracking the number of times one speaker talked after another in successive turns, the total number of turns for each user, the average topic distance between speakers, the a priori probability that a speaker will talk, and a moving window that tracks the information related to speakers' turns. The moving window maintains the most recent conversation turns within a duration equal to M. For each turn, it records the speaker's ID, speaking time, and word count. When the total speaking time in the moving window exceeds M minutes, the earliest turn is removed, and the latest one is added (FIFO queue).



 §.§ Control Policies

The information contained in the moving window has been used to develop two policies aimed at controlling different aspects of group dynamics.



  §.§.§ Balancing Policy

The Balancing policy exploits the data from the moving window to determine which speaker to address. Its goal is to identify and engage with the user who is least active in the conversation (i.e., the submissive user). Conversation participation is quantified using a metric D_i, which considers both speaking time and word count, as these are the most significant indicators of dominance <cit.>. To calculate D_i for each speaker S_i, we measure the percentage of their speaking time (T_i) and word count (W_i) within the moving window. The metric D_i is then computed as:

    D_i = γ_1 T_i + γ_2 W_i,

where γ_1 and γ_2 are weights representing the relative importance of speaking time and word count in determining dominance. The speaker addressed by the Balancing policy is S_m, where:


    m = min_i(D_i).


There are two versions of this policy: the “hard" version (BH) and the “soft" version (BS). In the hard version, if a user other than the intended one responds, the robot ignores the response and repeats the question to the intended user. In contrast, the soft version accepts responses from any user, replies accordingly, and then readdresses the intended user with the original question.



  §.§.§ Community Policy


The Community policy is based on the hypothesis that it is possible to identify sub-groups (i.e., communities) among participants in a conversation. To identify these communities, we use the probability that one speaker talks after another, assuming that members of the same community tend to speak consecutively. This probability is derived from the matrix in the dialogue state, which tracks the frequency with which each speaker follows another. This data is represented in a matrix and then converted into an undirected graph, where nodes represent speakers and edge weights represent probabilities. The Louvain algorithm is applied to this weighted graph to find the optimal partitioning of nodes into communities <cit.>. Once the best partition is identified, the policy uses this information to address a random speaker from a different community at each turn. The goal is to maintain a single cohesive conversation and prevent speakers from dividing into sub-groups. Similar to the Balancing policy, both a hard (CH) and a soft (CS) version have been developed for the Community policy.

Finally, it is important to note that the policy to be used by the system can be selected before the start of the interaction.



§ EXPERIMENTS AND RESULTS


The experiments were conducted at the “Parini Merello" middle school in Genoa, where we brought the humanoid robot Pepper, equipped with the CAIR cloud client. Approval was obtained from the University of Genoa's ethical committee, and consent forms were signed by the parents of all participating first and second-grade students. A total of 300 students participated, divided into 75 groups of four. Each group took part in an experiment where the robot used one of the five developed policies, resulting in five types of experiments with 15 groups per experiment.

To establish a baseline, 15 groups interacted with the robot using a Neutral policy (N). The remaining groups were equally split among the other four policies. Participants registered to CAIR using their voices and then interacted freely with the robot for 15 minutes. They were unaware of the specific policy in use and could respond at their discretion. This approach ensured spontaneous and natural interactions. Each experiment required approximately 5 minutes for registration and 15 minutes for interaction with the robot. To ensure the privacy of the participants, all their personal information, including name, gender, and biometric data related to their voice, was promptly deleted after each experiment.

For the control groups (N), no specific results are expected, as this baseline represents the robot not attempting to influence the conversation dynamics. In contrast, for the first and second experimental groups (BH and BS), we anticipate that the robot will successfully “balance" participation, ensuring all participants are included equally in terms of speaking time and word count. For these experiments, we set the weights γ_1 = γ_2 = 0.5. The third and fourth experimental groups (CH and CS) introduce the concept of community, aiming to demonstrate the robot's ability to unify any emerging sub-groups among the participants.

Figure <ref> illustrates the speaking times of four participants from two separate experiments using the N policy and the BS policy, respectively. The x-axis represents the turn number, while the y-axis shows the speaking time (in seconds) within the moving window. These results exemplify how, under the N policy, a dominant speaker often emerges, with the difference between the highest and lowest speaking times widening. This occurs because the robot does not direct the conversation, allowing extroverted participants to dominate. Conversely, when the robot uses the BS policy, even its soft version, the speaking times of participants remain more balanced.

It is important to note that the number of turns varies across experiments. This variation is primarily because, although all experiments have a duration of 15 minutes, participants may speak for different amounts of time or remain silent due to initial shyness or uncertainty about what to say. Typically, the number of turns observed in the experiments ranged from about 40 to 60.







Figure <ref> shows a comparison between two experiments where the robot applied the N policy and the CH policy. The x-axis represents the turn number, while the y-axis displays the number of communities formed among participants. Under the N policy, the number of communities never drops below three, indicating that each speaker often forms a separate community. Conversely, under the CH policy, the number of communities decreases from four to mostly one throughout the experiment.

Table <ref> presents the means and standard deviations of speaking time errors, word count errors, and the number of communities across all experiments using the described policies. The error is defined as the difference at each turn between the highest and lowest values among the speakers, both for speaking times and word counts. 

To determine statistically significant differences between experiments with different policies, we used the Shapiro-Wilk test to check for normal data distribution. As some data were not normally distributed, we applied the non-parametric Mann-Whitney U test to compare the results.

We aimed to verify if the Balancing policy improved speaker participation in terms of speaking time and word count. To do this, we compared errors in speaking time and word count for the Balancing policy (BH and BS) against the baseline policy (N). Results were statistically significant with p<0.01, indicating a significant difference between the Neutral and Balancing policies. The average speaking time error for N was 40, compared to 8 for BH and 7 for BS, with lower standard deviations for BH and BS. No significant difference was found between BH and BS.

Next, we tested if the Community policy reduced the number of subgroups among speakers by comparing the average number of communities in CH and CS with those in N. All comparisons showed p<0.01, indicating significant differences when using the Community policy. We also confirmed no significant difference between CH and CS in reducing the number of communities.

Finally, we found that the Balancing and Community policies resulted in comparable outcomes regarding the average number of communities. The Mann-Whitney U test showed no significant difference among BH, BS, CH, and CS. This suggests that the Balancing policy also reduces the number of subgroups among participants. While the Balancing policy may be the most effective, selecting the best policy requires considering additional factors like the robot's preferred interaction style and user experience.






§ CONCLUSION


This paper explored the effective management of group conversations involving a social robot. Motivated by the need for systems that handle multi-party interactions, the proposed system recognizes users, engages them in dialogue, and applies policies to ensure balanced participation and foster a sense of community. This system has diverse applications in healthcare, entertainment, and education, where multi-person interaction can enhance user experience.

We implemented and tested four control policies: Balancing Hard (BH), Balancing Soft (BS), Community Hard (CH), and Community Soft (CS). These were evaluated against a baseline in 75 experiments with a humanoid robot and 300 participants grouped in sets of four. The robot moderated the conversations, applying policies to balance speaking time and word count and reduce subgroup formation. Results showed that the policies effectively managed group conversation dynamics as intended.








































  






















































plainnat




 Paper Title: DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration.



DiscipLink]DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration



cb.zheng@connect.ust.hk
0000-0003-0226-9399

  Hong Kong University of Science and Technology
  Hong Kong
  China



yzhangiy@connect.ust.hk
0000-0001-8263-1823

  Hong Kong University of Science and Technology
  Hong Kong
  China



zhuangbi@connect.ust.hk
0000-0001-8199-071X

  Hong Kong University of Science and Technology
  Hong Kong
  China



chuhanshi@seu.edu.cn
0000-0002-3370-1626

  Southeast University
  Nanjing
  China



mxubh@connect.ust.hk
0009-0001-0591-7526

  Hong Kong University of Science and Technology
  Hong Kong
  China



mxj@cse.ust.hk
0000-0002-9847-7784

  Hong Kong University of Science and Technology
  Hong Kong
  China



§ ABSTRACT 
  Interdisciplinary studies often require researchers to explore literature in diverse branches of knowledge. 
  Yet, navigating through the highly scattered knowledge from unfamiliar disciplines poses a significant challenge. 
  In this paper, we introduce , a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) in interdisciplinary information seeking ().
  Based on users' topic of interest, initiates exploratory questions from the perspectives of possible relevant fields of study, and users can further tailor these questions.
  then supports users in searching and screening papers under selected questions by automatically expanding queries with disciplinary-specific terminologies, extracting themes from retrieved papers, and highlighting the connections between papers and questions.
  Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that can effectively support researchers in breaking down disciplinary boundaries and integrating scattered knowledge in diverse fields. 
  The findings underscore the potential of LLM-powered tools in fostering information-seeking practices and bolstering interdisciplinary research.







<ccs2012>
   <concept>
       <concept_id>10002951.10003317.10003331</concept_id>
       <concept_desc>Information systems Users and interactive retrieval</concept_desc>
       <concept_significance>500</concept_significance>
       </concept>
   <concept>
       <concept_id>10003120.10003121.10003129</concept_id>
       <concept_desc>Human-centered computing Interactive systems and tools</concept_desc>
       <concept_significance>500</concept_significance>
       </concept>
   <concept>
       <concept_id>10010147.10010178</concept_id>
       <concept_desc>Computing methodologies Artificial intelligence</concept_desc>
       <concept_significance>300</concept_significance>
       </concept>
 </ccs2012>


[500]Information systems Users and interactive retrieval
[500]Human-centered computing Interactive systems and tools
[300]Computing methodologies Artificial intelligence
























[
    Xiaojuan Ma
    August 5, 2024
==================





§ INTRODUCTION





The knowledge of humankind is classified into disciplines, and many researchers are trained and specialize in a single discipline <cit.>. 
However, there is a growing trend towards adopting interdisciplinary approaches to research, especially for complex, real-world problems <cit.>. 
Although forming research teams composed of experts from various fields is one preferred way of carrying out interdisciplinary studies, resource constraints and budget limitations sometimes make it impractical to establish such teams <cit.>. 
Researchers trained in one discipline often have to independently seek out information from the literature from various, maybe unfamiliar, research fields to develop their project ideas and approaches, a process we refer to as interdisciplinary information seeking () in this paper.
However, this process is challenging for several reasons.
First, relevant literature to an interdisciplinary project is often highly scattered in various weak-connected research areas <cit.>.
It often requires multiple iterations for researchers to identify what to search <cit.>.
Second, different disciplines use different terminologies, which becomes an obstacle for researchers to form accurate queries for searching literature in their unfamiliar fields <cit.>.
Also, interdisciplinary researchers face the challenge of interpreting vast amounts of information from various unfamiliar fields and evaluating the relevance to their interests during the search <cit.>.



While prior human-computer interaction (HCI) work has proposed various approaches for seeking scholarly information, few of them are targeted at the needs of .
For example, when a computer science researcher is looking to find existing work on computational approaches to combat misinformation, existing literature search tools can facilitate the search by expanding on citations and references <cit.>, iterating search keywords <cit.>, or tracking key actor activities <cit.>.
However, when the researcher is curious about what knowledge in sociology and education, in particular, can be blended into research on this topic, these tools provide limited guidance within the vast search space.

















In this paper, we aim to leverage the recent breakthroughs in Large Language Models (LLMs) to tackle the challenges of . 
LLMs, such as GPT4 [<https://openai.com/gpt-4>] and Gemini [<https://deepmind.google/technologies/gemini>], are pre-trained on vast amounts of internet-wide text data, enabling them to embed the knowledge from various academic fields and can potentially tackle challenges mentioned above <cit.>.
Various LLM-powered literature search tools have emerged, such as Consensus [<https://consensus.app/search/>], Perplexity [<https://www.perplexity.ai/>], and SciSpace [<https://scispace.com/>].
These tools generally retrieve relevant papers based on user questions and provide answers grounded on the retrieved papers, which lowers the risks of hallucination <cit.>.
However, current LLM tools might still not be a perfect solution for for several reasons.
First, interdisciplinary projects often target “ill-structured problem space” <cit.>. 
As a result, the search process is by nature exploratory, and the intents of researchers will change along with the search process <cit.>.
Automating the search with LLMs does not account for the shifts and leaves users with the burden of iterating exploration.
Second, existing workflows allocate the paper screening task entirely to LLMs.
However, the paper screening process is critical as interdisciplinary researchers need to make sense of outside domains <cit.> continuously, have serendipitous discovery <cit.>, and build cognitive contexts <cit.> from the process. 
Moreover, it is found that the information seeking by LLMs might be biased, creating a “generative echo chamber” <cit.>.
Thus, it is critical to engage researchers in the exploration process instead of solely relying on the responses provided by LLMs.




To better assist researchers in with LLM, we design and develop , a human-centered AI system to co-explore literature in various disciplines with users.
integrates a mixed-initiative <cit.> and human-AI collaboration <cit.> workflow.
With users' topic of interest as input, first generates exploratory questions (EQs) around the topic from the perspectives of various relevant disciplines.
In this process, LLMs simulate domain experts from specific fields to provide EQs.
Users can tailor the EQs or elicit to generate more EQs by either making direct requests or sharing papers they find interesting, thus guiding the system to uncover new directions for exploration.

Besides, when users want to delve into any specific EQs, surfaces the existing knowledge about the EQs for users.
Specifically, we design an LLM-based query formulation strategy by including discipline-specific terminologies in the queries to extensively search papers around the EQs while maintaining relevance to the intended field(s).
After retrieving papers from queries, extracts key themes that are related to users' topics of interest as well as selected EQs to facilitate users in finding answers for the EQs.
Additionally, annotates returned papers by highlighting their connection with users' exploration focus to facilitate users in efficiently screening papers.







We evaluated through two complementary studies.
First, we tested the usability of with 12 graduate students by comparing our system with a baseline condition on searching papers for specific interdisciplinary research topics.
The quantitative findings indicate that enabled participants to complete their tasks more efficiently and to uncover a more comprehensive range of knowledge.
In the second study, we invited seven experienced interdisciplinary researchers to use for literature exploration in their own projects.
The qualitative feedback from participants highlighted their appreciation for 's co-exploration workflow. 
Participants also identified potential limitations of , particularly in meeting the unique needs of individual researchers.

In summary, the contribution of this paper is threefold:









    
  * , an interactive system featuring a human-AI co-exploration workflow, empowering researchers in interdisciplinary information seeking.
    
  * A comparative experiment and an open-ended exploratory study to evaluate the effectiveness of .
    
  * Design implications derived from our design process and the user studies on how to support interdisciplinary researchers with LLMs.








§ RELATED WORK







In this section, we first introduce the key processes and challenges of interdisciplinary information seeking.
Then, we discuss the existing systems that support the literature review.
Lastly, we discuss existing human-AI collaboration research on information-seeking and sensemaking.



 §.§ Interdisciplinary Information Seeking ()



Interdisciplinary research involves integrating knowledge from multiple disciplines to address research problems <cit.>.
A critical component of this type of research is interdisciplinary information seeking (), the process of gathering relevant information across various fields.
Compared with normal scholarly information seeking, is typically more complex and time-consuming due to its highly dynamic process <cit.>.



<cit.> describe through a nonlinear model comprising three main stages: orientation, opening, and consolidation, which are often intertwined during the process (see <ref>). 
Orientation involves researchers determining their search direction.
The key challenge in orientation is the highly-scattered nature of the necessary knowledge for an interdisciplinary project <cit.>.
Researchers aim to construct an interdisciplinary knowledge picture for their projects, yet they cannot be experts in every field. 
Consequently, they often seek out resources, including experts, to discern relevant knowledge <cit.>. 
In the absence of such guidance, they have to rely on their own general knowledge and intuition to determine the disciplines from which to gather information <cit.>. 
This approach, however, introduces considerable uncertainty into their search process.
Furthermore, researchers often need to revisit their search goals based on insights from the opening and consolidation stages, adding to the time required for  <cit.>.

Opening is the stage where researchers explore literature in their chosen search directions.
Interdisciplinary researchers tend to deliberately increase the breadth of their search and maintain an open-minded attitude towards all encountered information, considering it potentially valuable <cit.>. 
This approach is driven by the necessity to gather widely dispersed knowledge inherent to .
A significant challenge in this stage is the terminology gap: researchers often struggle to craft queries that use appropriate terminologies to effectively bridge their research interests with literature from unfamiliar disciplines <cit.>.
Previous work suggests that terminology may “have a different meaning” <cit.>, and “not always appropriate or transferable” <cit.> across disciplines.
Researchers might not be able to immediately find the papers for the search direction they identified in orientation due to the terminology gap.
Thus, they typically need to gradually accumulate their knowledge in unfamiliar fields and iterative refining search terms to uncover relevant literature <cit.>.

Consolidation is the stage where researchers decide whether and how to include found papers in their projects, typically following the Opening and Orientation stages. 

Two primary challenges emerge during consolidation. First, the Opening stage results in the accumulation of a large volume of information, much of which may be irrelevant noise, thereby imposing a significant information load on researchers <cit.>. 
Second, the found papers have their own contexts in their belonging disciplines, and to sift, verify, and incorporate the found papers, the interdisciplinary researcher must make sense of the connection between the found papers and their own project
This task is challenging for researchers who are not accustomed to reading literature from unfamiliar fields <cit.>.




In summary, is challenging because of the highly scattered relevant knowledge, terminology gap, information load, and the difficulty in identifying relevant papers.
These challenges identified by prior work motivate our system design (see <ref>).



 §.§ Interactive Systems Supporting Literature Review


Getting a comprehensive understanding of prior works is generally challenging for researchers <cit.>.
Although survey papers synthesizing previous works into themes are commonly used by researchers to grasp fields <cit.>, it is uncertain whether existing surveys can effectively cover all fields and capture rapidly emerging research topics. 
To overcome the obstacles associated with literature reviews, some studies have aimed to simplify the process of digesting research papers <cit.>, while others have focused on aiding researchers in compiling relevant literature through mechanisms like citation networks <cit.> and identification of key authors <cit.>.

Specifically, <cit.> outline two primary strategies employed by prior interactive tools to assist researchers in synthesizing literature reviews from extensive bodies of work: the bottom-up approach, which supports researchers in developing key themes through improved navigation at the paper level, as seen in tools like Threddy <cit.> and Apolo <cit.>, and the top-down approach, which offers an overarching view of the literature, exemplified by Metro Maps <cit.>. 
Building on these concepts, <cit.> propose for a mixed-initiative approach that combines clustering papers via citation networks to provide an overview for researchers while also allowing them to customize this overview from the bottom up. 
Inspired by <cit.>, our system incorporates a mixed-initiative workflow once a collection of relevant papers has been assembled.

But differently, our design focuses on a more expansive search process across various disciplines to meet the unique demands of . Therefore, the system we propose takes users’ descriptions of their research interests as input, instead of using seed papers as in <cit.>’s work.
Moreover, we specifically tailor the discovery and synthesis workflow to preserve the link between discovered papers and the user's contexts, considering the potential diverge between them in .












Previous studies have also endeavored to enable researchers to uncover relevant knowledge from other disciplines, such as developing analogical literature search engines that facilitate the discovery of papers from distant fields sharing similar research aims <cit.>, directions or challenges <cit.>.

Nevertheless, the analogical search has a clear goal in performing literature-based discovery <cit.> to uncover solutions that could be transferred for solving the target research problem <cit.>.
Differently, our work targets a more expansive search process (i.e., ), and focuses on supporting researchers oriented to relevant research in various disciplines.
Additionally, <cit.> introduce a visual analytics system designed to automatically collate and synthesize research data from several disciplines, aiding in the selection of research directions in experimental medicinal chemistry. While their visual analytics method is tailored to a specific interdisciplinary domain, our study explores the broader potential of LLMs to facilitate the general processes involved in .







 §.§ AI-powered Information Seeking and Sensemaking
















Numerous initiatives have been undertaken to develop AI systems aimed at enhancing information-seeking and sensemaking activities. 
For instance, <cit.> introduced DocFlow, a visual analytics system that facilitates document retrieval and categorization through natural language questions within the biomedical field. 
<cit.> design in-context positioning of query suggestions and found it improves the exploratory search process.

The advent of large language models (LLMs) has positioned them as potential knowledge bases for information retrieval <cit.>. There is a growing body of research focused on refining the user experience in sourcing information from 
LLMs. For example, <cit.> argue that the typical linear conversational workflow found in many LLM-based tools falls short in aiding users to synthesize and comprehend information generated by LLMs. 
To address this, they advocate for the adoption of interactive hierarchical views to enrich information interaction practices. 
<cit.> present a novel approach by organizing LLM responses into interactive graph-based diagrams, thereby streamlining the process for users to pinpoint the information they need.



While LLMs in the above works mainly serve by directly generating answers for information seekers, hallucination remains a significant issue. 
To address this, some research focuses on using LLMs to extract and summarize information from search results. 
Selenite, an LLM-powered system recently proposed by <cit.>, employs LLMs to generate comprehensive overviews of options and criteria grounded in search results, guiding users through complex decisions.
Recent emerging works also see opportunities to leverage potential hallucinated LLM generations.
For example, <cit.> use LLMs to generate “hypothetical documents” for input queries to improve dense retrieval.
Similarly, <cit.> utilize LLM generation for visual search, but to help users better express their intents and refine their searches. 
<cit.> connect retrieved papers with users’ existing collections using LLMs, which may include hallucinations, to aid in sensemaking. 
They found that in their design, hallucinations had minimal negative impacts.
In our work, we also explore how potentially hallucinated generations can support information seeking.
We use LLMs to produce high-level exploratory questions, instead of facts, from various disciplinary perspectives.
These questions enable users to co-explore with LLMs.
Additionally, we leverage LLMs to aggregate discipline-specific terminology by generating pseudo-answers, thereby expanding search queries with relevant terminologies to help users bridge terminology gaps in .


























§ DESIGN GOALS

















Drawing on insights from prior research on interdisciplinary information seeking (refer to <ref>) and reflections from our design iterations (detailed in the supplementary material), we have identified four design goals (DG) essential for designing a human-AI collaborative system to aid in .


    
  DG1 Support orienting the exploration in various disciplines.
    Engaging in interdisciplinary research often necessitates acquiring knowledge from various disciplines, which challenges researchers in determining relevant search areas in unfamiliar fields <cit.>. 
    Although LLMs can assist researchers in delving into topics across various disciplines, their tendency to generate hallucinations may lead to confusion. 
    Additionally, prior studies <cit.> and our preliminary design experiments underscore the importance of maintaining high self-agency for researchers who prefer to be actively involved in the exploration process. 
    Accordingly, the system should not solely provide answers for with LLMs but should utilize LLMs as a knowledge resource, guiding users in their exploration and enabling a collaborative exploration experience.

    
  DG2 Support formulating search queries with domain-specific terminologies.
    Formulating effective search queries, especially with terminology spanning multiple fields, poses a significant challenge for researchers in  <cit.>. 
    This often involves iterative refinement of search keywords, informed by ongoing discoveries from the literature <cit.>. Given that LLMs excel in linking concepts and generating diverse query sets <cit.>, the system should leverage LLMs to assist users in crafting queries, thereby saving time and enhancing search efficiency.

    
  DG3 Organize retrieved papers around exploration focus.
    The sheer volume of papers encountered in can overwhelm researchers <cit.>. 
    Moreover, search results often include irrelevant noises that detract from the exploration focus, as revealed in our design iterations. 
    Previous HCI research suggests the aggregation of similar information and providing overviews is helpful in such cases to alleviate user burden <cit.>. 
    Therefore, the system should categorize retrieved papers into overviews, enabling users to swiftly pinpoint relevant groups of literature.


  DG4 Provide Information Scent for Efficient Paper Sifting.
In the process, researchers often need to meticulously review papers to verify their relevance to specific aspects of their research <cit.>. 
This labor-intensive task necessitates a mechanism for quickly identifying relevant information within papers. 
To facilitate this process, the system should highlight critical paper details and offer “information scents” that guide researchers in their review process <cit.>.


    
    
    
    
    
    
    
    
    
    
    
    
    
















§ THE SYSTEM

We introduce , a human-AI collaborative system designed to enhance interdisciplinary information seeking (). 
Unlike previous efforts in scholarly information seeking, focuses on enabling researchers to effectively collaborate with LLMs for broader exploration across disciplines, thereby linking their own projects with diverse knowledge.

Generally, inspired by prior research <cit.>, facilitates both top-down and bottom-up mixed-initiative literature discovery workflows (<ref>).
In the top-down workflow, users decide the exploration direction they are interested in first, avoiding being lost in the details of a number of papers.
To aid this process, generates diverse EQs from various disciplines based on the user's research topic (<ref>-1), crafts comprehensive queries for each EQ (<ref>-2), and summarizes key themes from retrieved papers to streamline screening (<ref>-3).
On the other hand, the bottom-up approach empowers users to spontaneously navigate through papers, citations, and references.
Users can organize the papers of interest and formulate new EQs based on these papers, thereby starting new research directions.
enhances this exploration by organizing citations and references based on user exploration history and suggesting EQs informed by user-selected papers.

As a research prototype, uses GPT-4 as the underlying LLM, API service for searching papers, and “” <cit.> for text embedding.
The subsequent subsections detail a usage scenario and describe 's key features in addressing our design goals.

























 §.§ Usage Scenario

Zoey, a computer science researcher, is tackling a new project aimed at raising awareness about social media misinformation among older adults. 
While she possesses expertise in misinformation detection through natural language processing, she finds herself stumped by the challenges older adults face in navigating social media and the human factors influencing misinformation awareness.
To bridge these gaps, Zoey turns her attention to unfamiliar disciplines like sociology and psychology for insights. 
Beginning with search terms such as “older adults misinformation,” through Google Scholar, she finds a mountain of papers that seem relevant across disciplines, including education, sociology, and psychology, each offering unique perspectives on the subject. 
Determined not to miss out on anything potentially useful, Zoey digs into each paper, one by one. 
However, the scattered insights across different fields make her research feel like assembling a vast puzzle without a guiding image.
Moreover, she needs to understand different perspectives during her search, each requiring a shift in mindset. 
Struggling to manage the information overload and the constant shifts in disciplinary perspectives, Zoey considers seeking help from .

Upon describing her research project to , Zoey receives a list of exploratory questions (EQs) organized by specific disciplines.
Zoey is particularly interested in the perspectives of psychology, education, and sociology.
These EQs, framed with easy-to-understand language, introduce some key factors, such as “What can older adults be motivated to learn digital literacy?” (education), “What cognitive strategies reduce belief in false information?” (psychology), that has never come to her mind before, possibly due to her unfamiliarity with these disciplines.
Among these, the EQ “How effective are peer-led education programs for seniors?” catches her interest. 
However, aiming to align it more closely with her focus on misinformation, she refines it to “How can peer-led education programs combat misinformation among older adults.”

After tailoring several EQs across psychology, education, and sociology to her preferences, Zoey prompts to fetch relevant papers. 
After searching, organizes the findings into themes for each selected EQ in the Exploration view (<ref>-middle).
Delving into the search results on cognitive strategies to counter misinformation, Zoey discovers themes such as “Critical Thinking and Cognitive Improvement” and “Inoculation and Overconfidence Strategies to Combat Misinformation”.
This overview allows her to navigate the themes without getting bogged down in the details of individual papers.

Attracted by the concept of “psychological inoculation,”, a term new to her, she explores a linked wiki page, uncovering its relevance in preemptively addressing misinformation. 
Through annotated key phrases in paper titles and abstracts, such as “misperception” and “credibility,” Zoey confirms the relevance of these papers, collecting them by dragging the theme to the Collection view (<ref>-right).



Later, Zoey's exploration leads her to a highly cited paper, and habitually, she checks its citations. 
categorizes these citations by discipline, with “Medicine” surprisingly ranking in the top three. 
Out of curiosity, she examines the citations under Medicine, and based on the highlighted terms, she finds a paper that discusses the impacts of health misinformation on older adults.
She realizes the strong connection between health misinformation and the older adults, but she hasn't explored this direction.
To extend her exploration, Zoey drags the paper to the orientation view (<ref>-left).
Based on the paper, recommends three new EQs around health misinformation's impact on older adults in the context of social media, thus broadening her exploration horizon.


















 §.§ System Design




  §.§.§ [DG1] Elicit Exploration across Disciplines


To support users in expanding their exploration to relevant information scattered in various academic fields (DG1), encapsulates potential literature search objectives into interactive elements named exploratory questions (EQs).
In other words, proposes EQs from different disciplines' perspectives to elicit users' breath exploration.
For example, when exploring a research topic of facilitating older adults to learn about AI tools, possible EQs could be “what cognitive challenges older adults may face in understanding complex concepts?”, which concerns psychology, and “how to design interactive tools to support learning for older adults”, which is more related to HCI literature.
Below, we introduce the enabled interactions around EQs in , the design rationale, and the EQ-generation process.


Supported Interactions around Exploratory Questions.
Upon users entering a research topic (<ref>-A1), generates a list of EQs tailored to the research topic of interest. 
In this process, we leverage the inherited knowledge of derived from its large-scale training data to ensure the coverage of diverse disciplines. 

Users have the flexibility to modify their topic description anytime, prompting to produce new EQs based on the updated description or to initiate a new exploration session by resetting the current ones (<ref>-A2). 
The EQs are categorized by their relevant disciplines (<ref>-D1), with a navigation bar allowing users to easily filter EQs by research field (<ref>-B). 
By selecting EQs, users can specify their exploration intentions for to identify literature closely aligned with their interests. 
Further interactions include editing EQs (<ref>-D2), adding or removing selected EQs (<ref>-C1), and generating additional EQs by specifying disciplines and particular needs or by manually entering questions (<ref>-C2).





Note that if users encounter an interesting paper on a yet-to-be-explored topic, they can obtain new EQs from based on the paper by dragging it from the exploration or collection view to the orientation view. 
Users have the option to pinpoint specific aspects of a paper, such as particular keywords, that pique their interest. 
Upon specifying these details, they can direct to suggest new EQs tailored to these focal points.
We set to provide three new EQs each time.
Users can choose one or several EQs to refine or opt to craft new EQs from scratch. 
Once these EQs are finalized and submitted, initiates a new round of search for papers concerning these EQs immediately.



Design Rational.
Our design of EQs draws inspiration from human-centered design principles, including the creation of a shared representation to balance automation with user agency <cit.>, and the reification of actions into objects for direct manipulation <cit.>.

EQs act as a bridge between users and the system, offering an intuitive means for users to convey their interests and for to interpret and further translate these interests into complex queries, thereby fetching a broad spectrum of relevant papers. 
We have explored other forms of LLM assistance in the design process for guiding and expanding user exploration in .
Initially, our prototype leveraged to generate research topic statements for users. 
However, this approach led to two main issues: the topics often contained inaccuracies due to LLM hallucinations, and they served more as end products of exploration instead of inspirations for users to explore.  


Another alternative we considered was utilizing to generate simple queries, similar to those users might input into search engines like Google Scholar. This method, while straightforward, can be inefficient in .
Effective queries for necessitate the use of discipline-specific terminology, which could be challenging for users to understand. 
To address the problems in the above two methods, EQs in are framed as questions rather than facts, aiming to expand the exploration space, and users can flexibly customize EQs to co-explore with LLMs. Also, EQs are crafted in user-friendly language as a high-level abstraction of research directions over low-level typical search queries. 

















Prompts Design.
takes two main types of inputs from users to generate EQs: the research topic of interest and/or the paper of interest, as illustrated in <ref>. To generate EQs from diverse disciplinary perspectives based on these seeds of exploration, we employ prompt-chaining and persona-prompting techniques <cit.>.


The process involves chaining prompts to guide in generating EQs. 

More specifically, given a research topic, we provide the list of disciplines used by for to determine which of them might be relevant.
The list includes primary disciplines, such as “computer science” and “psychology”.
then determines specific subfields within these disciplines (e.g., “developmental psychology” within “psychology”) that closely align with the topic of interest, enabling it to propose more targeted questions.
Then, for each identified field and subfield, our prompt to begins with, “You are an expert in field. A researcher is developing a research topic: topic. What knowledge from your field could assist in developing this idea? Present your advice in the form of questions.” After collecting EQs across different disciplines, we instruct to refine this list by removing any duplicates, resulting in the final EQs shared with users.

Preliminary experiments indicate that adopting specific expert personas enables to produce questions that closely match the disciplinary focus.
For instance, when exploring how AI tools can support older adults  learning in science without employing a persona, language models tend to produce questions centered on “AI for learning.” 
These questions focus predominantly on “AI,” steering the subsequent discovery process toward computer science literature rather than education. 
Conversely, utilizing persona prompting can generate EQs that lean more heavily towards aspects of older adults learning from an education perspective, such as “What motivates older adult learners in science education?”.


For generating EQs based on a paper of interest, perhaps discovered through navigating the references and citations of papers returned in the previous rounds of exploration, adopts a similar persona-prompting strategy. 
Here, the meta-information of the paper, including its title, abstract, designated fields, and keywords, is integrated into the prompt context, allowing users to specify keywords that highlight their areas of interest. Following this, we prompt to identify relevant fields within the primary disciplines and simulate experts in these fields to recommend EQs related to the provided context. 




  §.§.§ [DG2] Query Expansion to Retrieve Relevant Papers




When users express interest in a particular EQ, furthers the exploration by expansively generating numerous queries related to the EQ.
Next, it conducts searches for papers in using these queries concurrently.
In EQ generation, we guide to employ straightforward, easy-to-understand language so that users can quickly decide whether to dive into an EQ or not. 
However, when it comes to searching for papers, we instruct to use precise terminology and field-specific jargon relevant to the EQ to craft the search queries. 
This aims to fill the terminology gap of introduced in <ref>.

Moreover, we intend to retrieve a diverse set of papers using the output queries to allow to map out the knowledge landscape around the EQ.
We design a prompt to craft queries according to these requirements, as shown in <ref>.

To be more specific, we begin by requesting to provide (pseudo-)answers from various perspectives on the current EQ in the form of bullet points. 
Although there is a risk of hallucination, these responses, even when flawed, are found to contain keywords and concepts beneficial for formulating effective search queries, as noted by <cit.>.
Subsequently, compiles a list of relevant terms associated with each bullet point. 
These terms lay the groundwork for creating a set of targeted search queries,  which are later fed into paper search engines.
This process, inspired by <cit.>, helps to enrich the conceptual breadth and depth of inquiries into varied branches of knowledge.












  §.§.§ [DG3] Organize the Retrieved Papers of around EQs


It is challenging for users to go through the extensive papers retrieved from the queries, especially considering that many papers are likely from unfamiliar fields of study.
To alleviate this information overload, distills key themes from the papers, focusing on the connection to the user's current exploration context (i.e., the research topic and the selected exploratory question). 
 

Themes Extraction. 
While the papers retrieved from the expansive queries could contain many themes, users would be more interested in those more related to their current exploration context, i.e., the research topic and the selected EQ.
A LLM-based clustering pipeline (<ref>) is deployed to sift through papers and extract themes pertinent to the user’s interests.


Several LLM-based clustering techniques have been proposed by researchers, while trade-offs exist.
For example, LLooM <cit.> and ClusterLLM <cit.> produce high-quality clusters but need to operate at the data instance level – LLooM involves distilling info from each input data instance and ClusterLLM uses triplet comparisons – leading to increased time costs. 
To balance quality and time costs and enable interactive theme exploration in , our pipeline begins by generating contextualized embeddings for papers, proceeds to cluster these embeddings, and concludes with refining the clustering outcomes.


In the first step, we are inspired by the keyphrase expansion technique proposed by <cit.> to construct the contextualized paper embedding.
This technique concatenates original text embedding with keyphrase embedding related to user intents. 
Similarly, in , the contextualized embedding comprises text embedding of the paper's metadata (title and abstract) and embeddings of the paper's discipline, if matching the EQ's discipline, as well as key phrases from the paper's metadata resonating with the research topic and EQ. 

By considering whether the paper's discipline matches the EQ's discipline in the paper embedding, we add a soft constraint on clustering papers that fall into the same field of study. 
To identify relevant phrases from academic papers, we first convert key concepts C = {c_1, c_2, ..., c_m} from the research topic and EQs, along with phrases P = {p_1, p_2, ..., p_n} from the paper's metadata, into embeddings. 
A phrase p_i is considered relevant if its cosine similarity with any concept c_j, defined as sim(v_c_j, v_p_i) > 0.6, surpasses a predetermined threshold of 0.6 [This threshold is determined through empirical testing during development.]. 


In the clustering phase, we employ DBSCAN <cit.> to group the contextualized embeddings.
While, through constructing the contextualized embeddings, the algorithm is more likely to put papers relevant to the exploration context together, it can produce other clusters less related to the contexts.
Besides, the resulting clusters might still have many papers that making it challenging for users to quickly screen them.
To this end, we harness to curate the clustering results.
Specifically, we prompt with two questions for each cluster: “Is this cluster related to the research topic and the selected question?” and “Is there a clear division between papers that a subclustering would be helpful?”
Based on the results, we remove irrelevant clusters and decide whether to divide any existing clusters further.
We still keep the papers under the removed clusters in a “possibly relevant paper” list in for participants to browse if they want.
This design decision is driven by the observation that researchers would not only search on purpose but also be interested in serendipitous information discovery <cit.>.

Supported Interactions. 
Upon selecting an EQ for which papers have already been queried, presents the extracted research themes. 
It displays the disciplinary distribution and key phrases for papers within each theme (<ref>-E) and links the key phrases to their respective wiki pages via DBPedia Spotlight <cit.> when possible. This feature aids users in demystifying unfamiliar terms. Users can delve into themes to review the papers classified under them.






  §.§.§ [DG4] Support Sifting Papers

presents retrieved papers with their disciplines, titles, and meta information such as authors, published year, venue, abstract, and references as well as citations (<ref>-F1).
Users can also click a link button to navigate to the page of the papers for further investigation.

Besides, highlights the relevant phrases in the title and abstract that are aligned with the current exploration contexts, i.e.,  research topic as well as the selected EQ, as shown in <ref>-F1.
The method of extracting relevant keyphrases is the same as the one in the clustering process described in <ref>. 
Additionally, to prevent information overload in the user interface, only the sentence from the abstract deemed most relevant to the exploration context is displayed. 
Formally, given an abstract represented as a document consisting of a set of sentences D = {s_1, …, s_n} and the set of concepts in the exploration contexts C = {c_1, …, c_m}, the key sentence s^* is identified by:
s^* = max_s_i ∈ D|{c_j ∈ C |  exists a phrase relevant to  c_j  in  s_i}|.
Users can also expand the key sentence to see the full abstract at will.




  §.§.§ Other Features

Besides the aforementioned four features that directly target the four design goals, also includes some other features to facilitate the process.

Support Organization of Useful Papers.
allows users to drag and drop themes or papers from the exploration view to the collection view.
When dropping themes, a collection is created in with the same title as the themes and containing the same group of papers.
When papers are dropped, they would be added to collections based on their sources: from a theme or from citations and references of other papers.
Users can edit the title of the collections, move papers from one collection to another also through dragging and dropping, and delete collections.
This organization feature, together with the themes extraction feature, supports the mixed-initiative workflow proposed by <cit.>.

Ranking Citations and Reference.
displays the citations and references of a paper upon users' requests, and these citations and references are organized based on their disciplines (<ref>-F2).
In particular, we ranked the disciplines to prioritize the ones that contain relevant information to the exploration contexts but are less explored according to users' interaction history with the system.
This ranking aims to balance exploration and exploitation <cit.> and support DG1.
We define users' engagement level in each discipline as U_d = p_d + q_d, where p_d is the number of papers collected and q_d is the number of EQs queried in discipline d. 
The exploration score, E_d, is set to 1 / (U_d + 1) to encourage exploring lesser-engaged disciplines.
The relevance of each discipline, V_d, is computed as the average cosine similarity between the paper embeddings in d and the user-specified research topic embedding. 
The combined score C_d for ranking disciplines is simply computed as C_d = β V_d + E_d, where β is set to 1 in our prototype.
For papers under each discipline, we rank them by their cosine similarity score with the users' topic of interest.













































§ STUDY 1: USABILITY EVALUATION



To evaluate , we first conducted a usability study on with a focus on how useful and easy it is to use in helping researchers search papers regarding an interdisciplinary research topic.
Specifically, we explore the following research questions:

    
  RQ1 Can help researchers gather more comprehensive literature? 
    
  RQ2 Does improve user efficiency in the process?
    
  RQ3 How do users perceive the usefulness and experience of interacting with ??    









 §.§ Participants

We recruited 12 participants (six females, five males, and one prefer not to say) from social media. 
They are all postgraduate research students.
Eight participants have 1-3 years of research experience, three have 3-5 years, and one have 5-10 years.
Their research directions include HCI (6), visualization (2), machine learning (2), programming language (1) and cognitive science (1). 
All but one participant confirmed that they have experience in interdisciplinary research.
Additionally, five participants suggested they often use AI tools, including Consensus [<https://consensus.app/>], Elicit [<https://elicit.com/>], Perplexity [<https://www.perplexity.ai/>] and Paper Digest [<https://www.paperdigest.org/>], for searching literature. 




 §.§ Protocol


We adopted a within-subject experiment design.
Participants were asked to complete tasks under two conditions in a counterbalanced order:  the system condition (using ) and the baseline condition (using tools they usually use).
We prepare two interdisciplinary research topics for participants to perform :


  * Develop digital teachers to mentor high school students in rural areas, focusing on training their problem-solving capabilities. We refer to this task as AI for education in later texts.

  * Help designers use AR to creatively document intangible cultural heritage, ensuring its preservation for future generations. We refer to this task as AR for ICH in later texts.












Participants were instructed to widely survey papers from various academic fields for each topic.
In both conditions, participants were required to complete an outline in Google Docs as the task outcomes. 
In the outline, participants needed to list the research papers they found relevant and group them into topics.
Participants were also asked to write a short explanation of how the topics related to the given project.

Before the task starts in the system condition, we first provide a 5-minute tutorial on and give 5 minutes for participants to try .
Then, they are given 30 minutes to search papers and complete the outline for each task.
After each task, they are asked to complete a corresponding questionnaire.
Upon completing both tasks, we ask participants to share their experience in using .



 §.§ Measurement


To answer RQ1, we compare participants' outlines in the two conditions from three aspects:
statistics of the outlines, participants' subjective ratings, and ratings from external experts.

For the statistics of the outlines, we computed the number of topics and papers in each outline.


For the participant's ratings, in the questionnaire after each task, participants self-rated the outline's helpfulness, comprehensiveness, organization, and relevance using the 7-point Likert Scale.
Moreover, before each task started, we asked participants to tell us what they planned to search for during the task.

Subsequently, upon completion of the assigned task, we directed the participants to provide ratings indicating the extent to which they identified relevant literature they had planned to search, as well as the degree to which they discovered relevant literature that had not been part of their initial search strategy.
These ratings help to assess whether achieves the goal of expanding researchers' exploration scope.
For the expert ratings, we recruited two experts to score the outlines.
One is a final-year PhD student, and another is an assistant professor.
Both are HCI researchers and have experience in multiple interdisciplinary research projects widely covering AI, XR, education, and design.


They evaluated the participants' outlines while blind to the condition using a 10-point Likert scale by rating the following statements:

    
  * Overall outline helpfulness: “I found the outline is overall helpful for developing the research project.”
    
  * Outline comprehensiveness: “I believe this outline covers a comprehensive set of relevant topics.”
    
  * Topic relevance for each topic: “I found this topic relevant to the project.”
    
  * Topic support for each topic: “I found the topic to be well-supported by papers under it.”

We refer to <cit.> in designing these statements except for the outline comprehensiveness.
We add the outline comprehensiveness as a measurement as an important design goal of is to assist researchers in gathering highly-scatter knowledge.
Before their evaluation, experts first engaged in a session to discuss how to give specific points.  
Experts' given ratings were averaged as the final score.

To answer RQ2, we measure the task workload using the NASA task load index <cit.>.
We also measure participants' knowledge gain during the search process, as researchers typically need to learn knowledge from other disciplines on the fly in .
Participants rated their knowledge on the given research topic using a seven-point Likert scale before and after the task, and we take the change in self-rated knowledge as the knowledge gain <cit.>.
They also rate their self-efficiency during the task.

To answer RQ3, we adopt the technology acceptance model  <cit.> to assess the perceived usefulness, ease to use, ease to learn, workflow compatibility of , and participants' intention to use it.
Participants also rate their perceived helpfulness of to key stages of , including orienting to various academic fields, breadth searching papers, filtering irrelevant papers, and making sense of the connection between the papers and the focused research project.











 §.§ Findings

For statistical results presented in this section, we used the paired samples t-test for comparison between conditions, unless the Shapiro-Wilk test of normality indicated a deviation from normality. 
In cases of non-normal data, we utilized the Wilcoxon Signed Rank test.





  §.§.§ [RQ1] Quality of the outline

Participants in the system condition, on average, collected 5.08 topics (SD=2.28), more than the baseline condition (M=3.92, SD=1.51), but there are no significant differences (p=0.057).
There are also no significant differences regarding the number of papers (the system condition: M=20.58, SD=14.39; the baseline condition: M=17.83, SD=10.31; p=0.58).

The experts ratings indicated significantly higher outline helpfulness of the system condition (M=6.67, SD=1.09) than the baseline condition (M=6.04, SD=0.96, p=0.003).
Similarly, experts' ratings of outline comprehensiveness are significantly higher in the system condition (M=6.63, SD=1.23) than in the baseline condition (M=5.83, SD=0.69, p=0.02).
Nevertheless, there are no significant differences regarding averaged topic relevance and averaged topic support for the two conditions.

For participants' subjective ratings (shown in <ref>), there are no significant differences between the self-report outline helpfulness (p=0.16), organization (p=0.31), and relevance (p=0.18).
However, participants reported higher comprehensiveness of the outline in the system condition (M=5.00, SD=1.60) compared to the baseline condition (M=3.08, SD=1.31, p=0.002).
Besides, there is no significant difference between how well participants consider they found literature they plan to search (p=0.07).
But regarding how well they found relevant literature they originally did not plan to search, the system condition (M=6.08, SD=1.44) is significantly better than the baseline condition (M=4.67, SD=1.44, p=0.05).

These findings suggest that is beneficial in helping researchers to gather comprehensive literature from various disciplines as intended.



  §.§.§ [RQ2] Search process


Participants in the system condition reported significantly larger learning gains (M=2.50, SD=2.468) compared to the baseline condition (M=1.08, SD=1.56, p=0.02), suggesting helps users learn more during the exploration.
Also, they self-reported higher efficiency in the system condition (M=6.18, SD=0.94) compared to the baseline condition (M=4.33, SD=1.50, p=0.01).
Participants exhibited a significantly higher task workload in the baseline conditions based on the averaging score of the NASA TLX items (M=4.01, SD=0.74) compared to the system condition (M=2.93, SD=0.97, p<0.01).
For the individual items (illustrated in <ref>), the system condition is significantly better than the baseline condition in regard to the physical demands (p=0.01), performance (p=0.03), effort (p=0.02), and frustration (p=0.04).
These results suggest that improves participants' search efficiency, lowers their workload, and helps them effectively learn more in the process.





  §.§.§ [RQ3] User experience

Participants generally held positive attitudes towards the usefulness, ease of use, ease of learning, and workflow compatibility, as shown in <ref>.
Most participants appreciated the generated EQs.
P12 noted that “When I came to this new field, I didn't know where to go and how to search on Google. There might be some terms in this field, and then it [] could help me ask some questions. It is difficult for me to ask professional questions in this way.”

Nevertheless, some participants complained that provides too many EQs and thus adds load to users: “It has many [EQ] recommendations, and I may still need some efforts to screen them. I need more time to see if this is relevant, and it is unlikely to be completed very well in a relatively short time.” (P06).
P07 expects the EQs to be better connected, “There may be a connection or overlap between them, but it didn't tell me.”.

Besides, as illustrated in <ref>, there were mixed opinions among participants regarding 's effectiveness in filtering papers. 
Those who viewed positively highlighted its ability to deliver relevant themes and papers aligned with the selected EQs. (“I think the results of the query are very accurate. I can’t get such accurate results using Google Scholar because I feel that it only matches titles. You can search more accurately with this [system].”, P11) and appraise the keyphrase highlighting (“The papers provided by other search engines may not be relevant, and I don't know exactly what relevant keyword it has. But you can clearly see the fields, the key phrases [in ]”, P09).
However, some participants felt challenged by the volume of papers recommended, pointing out the difficulty of effectively screening them within the experiment time, “Because it recommends a lot, I still need manual effort to screen them. I can't complete it very well in a relatively short period of time.” (P6)

We also examined what tools participants used in the baseline condition.
We found that all participants used Google Scholar, while P6 and P10 used tools including LitMaps and Connected Papers to check citation networks of papers, and P01, P08, P11, P12 attempted AI-powered literature search tools including Consensus, Elicit, Paper Digest, and Perplexity.
Compared to , participants suggested the tools displaying citation networks offer better visual salience on key papers but provide limited assistance in guiding search directions.
P10 noted that “Some of them [papers] were clustered together [in the citation network], and then I took a quick look at those. [...] But I just picked the standout ones and need to read carefully to proceed, while your tool is much better for getting a comprehensive exploration quickly.”
Regarding AI-powered search tools, P01, P11, and P12 initially tried them but quickly reverted to Google Scholar due to a lack of helpful answers. Conversely, P08 found Paper Digest useful for creating outlines but felt that “Your system has several exploration levels, allowing me to understand the paper I gave better. In the summary generated by Paper Digest, I am more about reading and accepting papers, and my understanding of these papers may not be as clear.”
This feedback illuminates the unique strengths of in unfolding the process.



§ STUDY 2: CASE STUDY

Encouraged by the results of study 1, we further conducted an open-ended exploratory study with experienced interdisciplinary researchers to get qualitative feedback on the potential benefits and limitations of in .





 §.§ Methodology



  §.§.§ Participants

We recruited seven researchers (two females and five males) who are focused on interdisciplinary research, including one assistant professor, two postdoctoral researchers, and four senior PhD students.
Participants are from various backgrounds, including AI for healthcare, data storytelling, social psychology, AI for science, music technology, and AI for education.



  §.§.§ Protocal


We recommended that participants choose projects for which they haven't fully devoted themselves to searching for relevant literature yet, in order to explore those projects in this study.
If they do not have such projects or they feel uncomfortable doing that, participants could also explore papers under their other interdisciplinary projects.
Participants were informed that none of the information on their research projects would be reported.

After getting the consent of participants, at the beginning of the study, we gave a tutorial on , as the one in Study 1, and suggested participants get familiar with the system through a 5-minute practice with a given research topic.
We then asked participants to explore their interdisciplinary projects using .
We encouraged participants to think aloud and explore for at least 20 minutes.
Participants are allowed to explore for a longer time if they are willing to do so.
After the open-ended exploration, we conducted semi-structured interviews with participants.
The interview questions cover participants' general experience with , whether helps in uncovering relevant knowledge in multiple disciplines, whether found helpful papers, whether helps in screening papers, and how well can fit into their daily process.
We also asked whether participants felt the level of automation was appropriate.




  §.§.§ Data Collection and Analysis

All study sessions were audio-recorded with the informants' consent. 
The audio recordings were transcribed using automated tools and subsequently manually verified.
Two members of our research team analyzed the transcripts using thematic analysis <cit.>. The coding results were thoroughly discussed and integrated through iterative discussions.



 §.§ Findings

We report our findings based on the participants' qualitative feedback, focused on those not mentioned by participants in Study 1.
Our reports do not include information about participants' research projects to protect participants' privacy.



  §.§.§ Helpfulness of EQs

All participants found some EQs generated by relevant and provided insights to their research projects, enhancing their literature exploration process.
P7 considered EQs “introduces a creative and playful way to pick your sense of curiosity.”
P3 specifically noted that the EQs aligned closely with the fields most pertinent to his research and matched the disciplines associated with the references cited in his paper.
He noted how exploring these EQs can be helpful to his paper: “There are some [EQs] that are relevant and can be helpful for developing the introduction part of the paper. There are also three or four questions that can be explored to develop follow-up future studies or some other discussions.”

Besides, P4 and P7 both mentioned that the generated EQs would particularly be beneficial to researchers in the onboarding stage of interdisciplinary research.
P4 found the EQs are related to “the core knowledge in each field”; thus “it is very helpful for understanding knowledge for first-year Ph.D. students.”
Similarly, P7 commented that “most of the researchers, including myself, have problems with the onboarding process. So, these types of applications or surveys could help us with the onboarding process and also learning the new research area step by step.”

Moreover, most participants found that streamlines the workflow that aligns with and augments their original workflow.
P1 noted that:


    In the past, I would think of directions in different disciplines and search for them. This process was in my mind, but it would definitely not be so comprehensive. It () would ask some very related questions that I had not thought of before.


An exception is P6, who usually did not search literature by himself.
He collaborated with experts from other disciplines and read the literature they suggested.
However, he also found helpful in his workflow:

    Sometimes, you don't want to follow the advice of experts exactly. What experts say is not always correct. [...] This tool is very useful when I want to develop something new [beyond their strengths].




  §.§.§ Various expectation of EQs


Although participants generally found EQs helpful, they also found some EQs did not meet their expectations.
P4, whose background is chemistry and physics, found that all EQs start with “How” or “What”, but not “Why”.
He considered “asking why” is more important as “
Understanding the underlying causes is important to conducting my research”.

Another concern is about the connection between the generated EQs and their projects.
Interestingly, there are diverging opinions from participants on this.
P1, P3, and P6 consider some EQs as not good because the connection between these EQs and their projects is loose and only covers a portion of the key concepts in the projects.
P1 noted that “some questions are simply digging into one field, and it seems that they are less relevant to my interdisciplinary project.”

However, P5 and P7 desired more EQs to delve deeply into other disciplines and not strongly connected to their own projects.
One reason is the lack of existing research on their project; thus, EQs strongly connected to the project “won't be able to help me find something very useful.” 
Another reason is the need to delve deeply into other disciplines of research, as explained by P5 with her previous experience:


    When I was doing [a certain concept] [related interdisciplinary research], I had to read a lot of psychological literature to see how psychologists used [a certain concept]. [...] If I don’t read real psychological papers and do it directly, then I will always read second-hand literature. I'll never know why people chose that model, and it is problematic.





Moreover, some participants believed the provided EQs should be different in different stages of the interdisciplinary research.

P2 elaborated on the development of the exploration needs and suggested adding more customization over the EQs generation:


    When you first explore this [interdisciplinary] field, you can have more possibilities [...] But I'm actually pretty sure I only want papers with [a concept] and [a concept]. I think before [providing EQs], I can have a choice. For example, am I a person who is exploring this [interdisciplinary] topic for the first time, or am I actually an expert in this topic?












  §.§.§ supports human-AI co-exploration


Participants generally considered the level of automation to be appropriate, and they were able to engage and direct the exploration.
We observed how participants actively tailor the EQs for their own goals.
For example, P1 edited EQs by adding concepts more related to his project to strengthen the connection between the found papers and the project; for P7, the original question was about how an intervention affects an element, and P7 edited the question by adding more elements could be affected to enlarge the search scope.
Participants also initiated the creation of EQs based on their insights during the exploration process.
P1 found “gamification,” and P2 found “music” as elements that they hadn't considered in their own projects but were relevant through browsing retrieved papers, and they created EQs based on these findings.

Additionally, P7 highlighted how the language framing of the EQs encourages him to tailor the EQs and join the co-exploration:


    If your system was giving me very sophisticated questions. Then, it might restrict my own creativity. But because the questions are broad, easy-to-understand. I think it helps me also think by myself. And these [EQs] can act like hints.


We also found most of the participants not only rely on the provided themes and the corresponding papers in to seek information, but also deliberately attempt more ways to discover papers, including digging into the “possibly relevant paper” list, the citation and references of papers, and even browse the recommended paper list in the paper page after they clicked the link of papers in .
This behavior aligns with the nonlinear model that researchers would deliberately increase the breadth of the search in  <cit.>.

When asked about their sense of control during the exploration process with , all participants affirmed they were in control. 
They attributed this to having a clear objective for their exploration to advance their own projects. 
Thus, their engagement with the system's recommendations was driven not by the system's guidance alone but by the perceived value of these suggestions to their projects. 
P6 encapsulated this sentiment by stating, “I have been deeply thinking about this research topic for a while. It's always on my mind. AI cannot diminish my goal or change my focus.”



















§ DISCUSSION



This paper explores the potential of LLMs in supporting interdisciplinary information seeking and contributes to the growing body of research on human-LLM interaction across various domains <cit.>. It is worth noting that there are differing opinions in academia regarding the role of LLMs in enhancing search systems.
<cit.> envision that future search systems can build on LLMs to offer answers to user queries instead of references that require users to further explore.
Conversely, <cit.> argue against “dropping a LLM-based agent as a one-size-fits-all solution” and emphasize the importance of user interaction with information in search processes, which enables activities such as learning and serendipity.
Our work aligns with <cit.>'s perspective by offering a human-AI co-exploration solution that integrates LLM support while enabling rich user interaction during the search process.
In this section, we discuss the lessons learned in designing and evaluating , as well as its limitations and potential directions for future work.



 §.§ Exploratory Questions as Share Representation in AI-powered 

In , we conceptualize exploratory questions as a shared representation between users and AI to facilitate co-exploration. 
The concept of shared representation, as introduced by <cit.>, refers to the use of a representation, be it textual or visual, in human-AI interaction that is mutually intelligible to both humans and AI. 
This shared foundation allows for collaborative contributions to the task at hand, thereby striking a balance between automation and user agency. 
Participants in Study 1 infrequently modified the EQs, possibly due to time constraints. 
However, during open-ended exploratory studies, we noticed participants customizing the generated EQs and creating their own, showing the potential of EQs to act as a shared representation that encourages engagement from both humans and AI.

Feedback from participants in our first two studies was generally positive regarding the utility of EQs. Yet, some noted the lack of visible connections between EQs, particularly when they span across disciplines and overlap in themes after querying. 

currently does not highlight these links by presenting EQs in a list.
To address this, future efforts could consider better supporting the curation and exploration of EQs.
One possible direction is to consider the existing research on curating examples for creativity support, and visually contextualizing the EQs when they are generated <cit.>.



 §.§ Support Learning with Human-AI Co-Exploration

As noted in the literature <cit.> and commented by our participants in study 2 (<ref>), learning in helps researchers conduct interdisciplinary research over a long period of time.
This insight underpins our decision to create a human-AI co-exploration workflow rather than merely employing AI agents to autonomously compile research papers for scholars <cit.>.
Our approach is in line with the idea of AI aiding human cognitive processes <cit.>. 
Feedback from study 2 indicates that not only piques user curiosity but also facilitates their initiation into interdisciplinary research, thereby supporting learning within .

However, much of the existing research on human-AI collaboration has prioritized enhancing collaboration to boost immediate performance outcomes <cit.>, often focusing on short-term metrics such as the accuracy of decision-making with AI support. While these immediate results are valuable, we advocate for a broader focus within the field, emphasizing support for enduring outcomes of human-AI collaborations. This includes promoting long-term project success (as in interdisciplinary research), enhancing worker well-being, and contributing to societal benefits.




 §.§ Contextualization in 


Interdisciplinary research often involves sifting through a vast array of potentially relevant papers. However, the specific contexts of researchers guide them in concentrating on particular studies. 
acknowledges this by using the user-provided research topic and selected EQs as a framework for offering contextualized support: recommending EQs tailored to the research topic and synthesizing themes from the papers based on these EQs.
Feedback from user studies confirms the relevance of EQs to their research topics and the extracted themes to the EQs.

Yet, findings from our Study 2 also indicate that interdisciplinary researchers expect to grasp their research contexts in a more nuanced way, beyond just the topics at hand. 
This includes understanding their preferences regarding the framing of questions (e.g., P4 shows a preference for “why” questions over “what” and “how”), their desired exploration scope (e.g., some participants favor an in-depth exploration into other disciplines while others prefer a stronger linkage with their own research project), and their current phase in the research process (e.g., P2 suggested questions could initially be broader and become more targeted as the research progresses). 
Although allows for customization of EQs, offering more nuanced EQ recommendations could significantly enhance the user experience by saving time and boosting efficiency.
Therefore, future endeavors should delve deeper into the specific factors of the needs of interdisciplinary researchers to offer more finely tuned exploration and research support.












 §.§ Limitation and Future Work








  §.§.§ Deepening the Support to the Nonlinear Exploration Process

The design of is motivated by the nonlinear model of behavior <cit.>.
supports the nonlinear exploration process primarily through generating EQs for orientation, query expansion for opening, and theme extraction for consolidation.
However, support for researchers could be further extended, especially in the consolidation stage and the transitions between different stages.
For example, does not provide direct supports for activities in consolidation such as incorporation and verifying, which is challenging due to the diverging tastes and paradigms across disciplines <cit.>.
Future work can explore methods to help researchers understand how to use the found papers effectively.
One inspiration could come from research on analogical search <cit.>, which supports researchers in transferring solutions from other disciplines. 
Also, improving the interdisciplinary reading experience through features such as guiding questions or in-situ summarization as suggested by  <cit.> can be helpful.

Moreover, participants indicated that is limited in assisting researchers in exploring the citation networks of papers of interest, which could facilitate transitioning from consolidation to opening or orientation. Existing work, such as Synergi <cit.> and Relatedly <cit.>, can enhance this support. For example, Synergi can aid in theme extraction from citation networks based on text snippets of seed papers <cit.>.








  §.§.§ Scale of the user study

In our usability experiment, the task scenarios are mainly related to HCI research.

We acknowledge that this design may limit the generalizability of our findings to other research contexts. 
Testing in a broader range of scenarios quantitatively would enable us to better evaluate its efficacy in supporting .
Additionally, both the usability experiment and the open-ended exploratory study are relatively short in comparison to the substantial amount of time typically required for researchers in .
Long-term field studies are needed to fully assess 's support in , and benefits as well as limitations of human-AI co-exploration.
For example, some participants in study 2 exhaustively explore all presented papers by the system, which can be explained by their open-minded search habits <cit.>.
It would be interesting to see whether long-term usage of would change such habits. 







§ CONCLUSION

In this paper, we present , a novel interactive system designed to enhance the process of interdisciplinary information seeking through human-AI co-exploration. leverages the capabilities of large language models (LLMs) to assist users in their exploration efforts by generating exploratory questions (EQs) and facilitating automatic query expansion. It enables users to actively participate in the exploration by customizing EQs. Additionally, employs contextualized clustering to extract themes from the gathered papers and annotates key phrases within those papers, aiding users in quickly identifying how these papers relate to their research objectives.
Our evaluation of includes two user studies. The first study assesses usability, gathering both quantitative and qualitative feedback from 12 researchers, which indicates that significantly enhances researchers' ability to conduct thorough paper searches. The second study examines the potential integration of into the daily routines of interdisciplinary researchers, with qualitative findings highlighting the value researchers find in co-exploring with . However, these studies also highlight certain limitations of , pointing towards areas for future investigation and improvement.





















This work is supported by the seed fund of the Big Data for Bio-Intelligence Laboratory (Z0428) from The Hong Kong University of Science and Technology. We thank the anonymous reviewers for their constructive comments and our user study participants for their efforts and insights. We also thank Yujie Zheng for helping with the literature review and Chengzhong Liu for providing suggestions during the early system design stage.





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§ EXAMPLE OUTPUTS OF PROMPTS FOR EXPLORATORY QUESTION (EQ) GENERATION AND SEARCH QUERY EXPANSION











We design to address the terminology gap by generating simple EQs to ease sensemaking and search queries with specific terminology to ensure search proficiency.
In this appendix, we provide example outputs of the generated EQs and queries.


 §.§ EQ Generation


uses EQs to help users explore unfamiliar fields by asking LLM to take expert personas and adding a condition in the prompt that the generated EQs need to be easy to understand.
We tested prompts for EQ generation, including the one used in , and two ablation prompts: 1) a prompt without persona; 2) a prompt without the simplification condition.
The prompts are presented in <ref>.

We used these prompts to generate three EQs under three different relevant disciplines for eight research topics (in total 72 EQs). Part of the results are presented in <ref>.
We include all the results in the supplementary material.
These results present hints that using persona makes EQs more concrete to domains and adding a simplification requirement enhances the comprehensibility.






 












 §.§ Search Query Expansion






first generates pseudo-answers (PA) and then uses PA to prompt for queries (<ref>).
We tested query expansion with PA generation first and without PA.
For each EQ generated in <ref>, nine search queries were generated.
Part of the results are presented in <ref> and all are included in the supplementary material.
We also compared the concreteness of the generated queries of the two query expansion approaches (with and without PA) using the MRC Psycholinguistic Database <cit.>.
The average concreteness ratings of the queries generated with PA is 319.56 (SD=52.37), and for the queries generated without PA is 292.42 (SD=39.69).
A comparison between the two conditions regarding the generated queries of each input EQ using paired t-test shows the queries generated with PA is significantly more concrete than the ones generated without PA (p<0.01).
These results provide evidence on the addition of PA helps inject domain-specific terms into the queries.



















