# Large Language Models are Fixated by Red Herrings: Exploring Creative
Problem Solving and Einstellung Effect using the Only Connect Wall Dataset

Saeid Alavi Naeini1,3 Raeid Saqur2,4 Mozhgan Saeidi2,4,6 John Giorgi2,4,5
Babak Taati1,2,3,4
1KITE Research Institute 2Department of Computer Science, University of
Toronto
3Institute of Biomedical Engineering, University of Toronto 4Vector Institute
for AI
5Donnelly Centre for Cellular & Biomolecular Research, University of Toronto
6Department of Biomedical Data Science, Stanford University
{saeid.alavi<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

The quest for human imitative AI has been an enduring topic in AI research
since its inception. The technical evolution and emerging capabilities of the
latest cohort of large language models (LLMs) have reinvigorated the subject
beyond academia to the cultural zeitgeist. While recent NLP evaluation
benchmark tasks test some aspects of human-imitative behaviour (e.g., BIG-
bench’s ‘human-like behavior’ tasks), few, if not none, examine creative
problem solving abilities. Creative problem solving in humans is a well-
studied topic in cognitive neuroscience with standardized tests that
predominantly use the ability to associate (heterogeneous) connections among
clue words as a metric for creativity. Exposure to misleading stimuli —
distractors dubbed red herrings — impede human performance in such tasks via
the fixation effect and Einstellung paradigm. In cognitive neuroscience
studies, such fixations are experimentally induced by pre-exposing
participants to orthographically similar incorrect words to subsequent word-
fragments or clues. The popular British quiz show Only Connect’s Connecting
Wall segment essentially mimics Mednick’s Remote Associates Test (RAT)
formulation with built-in, deliberate red herrings, which makes it an ideal
proxy dataset to explore and study fixation effect and Einstellung paradigm
from cognitive neuroscience in LLMs. In addition to presenting the novel Only
Connect Wall (OCW) dataset, we also report results from our evaluation of
selected pre-trained language models and LLMs (including OpenAI’s GPT series)
on creative problem solving tasks like grouping clue words by heterogeneous
connections, and identifying correct open knowledge domain connections in
respective groups. The code and link to the dataset are available at
https://github.com/TaatiTeam/OCW.

##  1 Introduction

The remarkable capabilities of state-of-the-art large language models (LLMs)
[82], across a variety of domains and downstream tasks [71, 8], have spurred
their comparisons with artificial general intelligence (AGI) [4, 12] and
human-imitative AI [27] systems. The extraordinary leap in capabilities of
these LLMs over a short span — from the advent of transformer-based [62] pre-
trained, context-aware language models (PLMs) [45, 15, 35, 31, 46] circa 2018
to 2020, to the current and latest cohort of increasingly larger (billions of
parameters) LMs [52, 70, 50, 80, 14, 60, 16] spearheaded by the OpenAI’s GPT
series [11], notably ChatGPT [43] and GPT-4 [42] — justifiably warrants such
comparisons. Several natural language processing (NLP) benchmarks have been
proposed to standardize the evaluation of these LLMs, including MMLU [24],
BIG-bench [59], HELM [33], and Global-Bench [58]. The tasks inventory under
these benchmarks are open (type of tasks) and dynamic (rolling additions).
While a subset of these tasks aims to test for human imitative intelligence
(e.g., nineteen tasks listed under the human-like behaviour category in BIG-
bench), none tests for creative problem solving abilities [39] — a hallmark of
human-like intelligence [27].

Creative problem-solving by humans is a well-studied topic in cognitive
neuroscience and human behavioural sciences literature. These studies and
methods use (word) associative fluency to model and test creativity
objectively [39, 7]. Empirical research in this context commonly employs
single or continuous word association tests that are variants of Mednick’s
seminal Remote Associates Test (RAT) [40]. Such tests entail finding
connections or links among a presented group of words using associations that
can be heterogeneous (e.g., synonymy, semantic, compounding) [77, 38]. To
exemplify, consider the cue words: {Tennis, Same, Head}. A correct connection
in this triplet is: Match, which connects by semantic link (tennis match),
synonymy (same match), and compounding (match head). Further, the word
connections can also vary in degrees of figurativeness (e.g., Star-Actress vs.
Star-Planet) and abstractness (e.g., Humor-Sense vs. Apple-Tree). In humans,
such creative problem-solving abilities are impeded by exposure to wrong
answers [54, 55, 76] — a finding referred to as the fixation effect [29, 74].
A closely related similar concept is the Einstellung effect [37], which
postulates the negative effect of previous experience when solving new
problems.

Figure 1: Examples of Only Connect walls with ground-truth groupings (rows)
and connections (last column). Red herrings include orthographically same
words (e.g., Gala) in different connected groups (Gala night, Apples, Swimming
gala) across walls. In Wall A (left), words Churchill, Marx, Castro provide
misleading stimuli inducing plausible fixation on historical figures within
the wall.

Studies examining the fixation effect induce fixations by presenting clue
words intended as wrong answers (misleading stimuli) [54] dubbed “red
herrings” or, by pre-exposing participants to red herrings before attempting
creative problem-solving tasks like the RAT [40]. A slew of works in negative
transfer learning in human cognition attempt to explain the RAT fixation
phenomenon that involves pre-exposure to red herrings by the negative effects
of prior learning on indirect or implicit measures of memory [56]. This
negative transfer effect was demonstrated and studied using orthographically
similar words to subsequent test word fragments as red herrings [56].
Intuitively, the red herrings lead participants away from the memory retrieval
(or down incorrect neurological pathways by Hebbian terminology) required for
correct responses and fixate on wrong connections [53]. Fixation in creative
problem-solving can be increased by making red herrings more retrievable.
Thus, creative problem-solving can be thought of as a type of indirect memory
measure whose retrieval is degraded by red herrings due to the negative
transfer effect. The red herring retrieval hypothesis states that factors that
make red herrings more retrievable should reduce creative problem-solving
performance, as measured with RAT problems. Two such factors are repetition
and context. A following corollary states that the memory strengths of red
herrings determine the magnitude of a fixation effect [6].

In this work, we study the juxtaposition of these theories from human
cognitive neuroscience (fixations, negative transfer learning, red herring
memory retrieval hypothesis) from the context of LLMs and natural language
processing. While negative transfer learning has been observed and studied in
AI research [69, 19], the context of these studies is limited to strict
machine learning sub-domains like statistical distribution measures and
computer vision. There has not been any work that systematically examines
these specific concepts’ relation in AI research. Our major contributions
follow:

#### 1\. Only Connect Walls (OCW) Dataset and creative problem solving tasks.

We introduce a novel dataset for evaluating creative problem solving tasks by
curating the problems and human performance results from the popular British
quiz show Only Connect [73]. Specifically, the Connecting Walls segment of the
show, where the tasks entail grouping presented sixteen (16) jumbled up clue
words into four (4) connected groups, and naming the correct connections
(Figure 1). The presented words have heterogeneous connections with open-
domain knowledge retrieval, e.g., history, places, famous people, tools, and
cultural references. These words contain red herrings or misleading stimuli by
design, which makes this dataset an analogical proxy for RAT tests in
evaluating LLMs for creative problem-solving. Section §2 provides a detailed
description of the dataset.

#### 2\. Experiments, results, and key findings of baseline LLMs evaluation.

We evaluate a suite of NLP models from static embeddings to PLMs to LLMs and
demonstrate that none of them are able to solve the tasks on the OCW dataset.
Our findings show SOTA LLM results to be significantly behind human baseline
performance. A surprising finding shows increasing in-context examples in few-
shot in-context-learning on GPT-4 [42] to be ineffective. Sections §3 and §4
provide details.

##  2 Only Connect Walls Dataset

Here we focus on the Connecting Walls segment (usually the third round) of the
quiz-show. Each wall contains sixteen jumbled-up word clues that must be
sorted into four groups, each with four connected words. Once the groups are
formed, contestants must also identify the right connection or relationship
among the items in each group. While there is only one correct solution to
each wall, the puzzles are designed to include several red herring clues that
can fit into another category and/or red herring categories fitting multiple
clues. Figure 1 shows solved sample walls from the show highlighting a couple
of typical red herrings.

###  2.1 Dataset Collection and Structure

The OCW dataset contains 618 connecting wall puzzles and solutions in total
from fifteen seasons of the show. Each show episode has two walls. The total
number of walls per season varies based on the (varying) number of aired
season episodes. The walls were scraped from fan websites111The primary source
was the Only Connect fan website: https://ocdb.cc., and human performance
results (for grouping and connection tasks) were manually curated by watching
all the episodes. Figure 2 depicts the high-level structure of the dataset in
JSON format with self-explanatory object keys and comments.

Figure 2: JSON Structure of the OCW dataset. One truncated example is shown.

###  2.2 Tasks and Evaluation Metrics

The two dataset tasks: Task 1 (Grouping), and Task 2 (Connections) are
identical to the quiz-show’s human participant tasks. We evaluate Task 1
(Groupings) via six metrics: number of solved walls, number of correct groups
(max. four per wall), Adjusted Mutual Information (AMI) [64], Adjusted Rand
Index (ARI) [25], Fowlkes Mallows Score (FMS) [18], and Wasserstein Distance
(WD) [47], normalized to $(0,1)$ range, between predicted and ground-truth
labels [79, 63].

We similarly evaluate Task 2 (Connections) with three metrics: exact string
matching, ROUGE-1 F1 [34], and BERTScore F1 [81]. Exact match is the most
strict, assigning a score of 1 when the predicted connection is identical to
the ground-truth connection and 0 otherwise. ROUGE-1 F1 relaxes this criterion
and will be large when there is a high proportion of ground-truth tokens in
the model’s predicted connection and a low proportion of non-ground truth
tokens. BERTScore F1 is similar but further relaxes this criterion, assigning
a non-zero score for semantically similar (but non-identical) predicted
tokens. Together these three metrics provide a more holistic view of model
performance on Task 2 than any one metric alone. Empirically, we find that a
ROUGE-1 or BERTScore F1 of $\geq 0.5$ indicates that a predicted connection
would likely be considered correct, and under $<0.5$ indicates it is likely to
be considered incorrect (Table 1). Note that BERTScore has many parameters
affecting the final score; a hashcode is produced and reported for
reproducibility.

Each of the evaluation metrics for Task 1 of Task 2 could be calculated per
wall, per episode, per season, or for the entire test set. We present results
on the entire test set in this paper (§4). We split the dataset into a train
set (62 walls), validation set (62 walls), and test set (494 walls). The
primary goal of our dataset is to evaluate the zero- and few-shot creative
problem-solving abilities of LLMs; as such, we elect to set the size of the
test set to be much greater than train or validation sets.

Table 1: Task 2 (Connections) examples of predicted and ground-truth
connections and their performance according to the chosen metrics. The exact
match will be 0 for anything but identical strings. Empirically, we observe
that a ROUGE-1/BERTScore F1 of $\geq 0.5$ indicates that a predicted
connection is likely correct.

Predicted Connection | Ground-truth Connection | Exact Match | ROUGE-1 F1 | BERTScore F1
---|---|---|---|---
Types of numbers | Types of numbers | 1.00 | 1.00 | 1.00
Slang terms for money | Slang for money | 0.00 | 0.86 | 0.79
Types of trees | Trees | 0.00 | 0.50 | 0.63
Bridges in London | Thames bridges | 0.00 | 0.40 | 0.31
Medieval occupations | Chaucer characters | 0.00 | 0.00 | 0.15

##  3 Experiments: Language Model Evaluations

This section describes methods and models used to provide baseline results for
the dataset. For Task 1 (Grouping), we use clustering techniques on word-
embeddings from classical and pre-trained language models (PLMs) (§3.1), and
few-shot in-context learning (ICL) with LLMs (§3.2). For Task 2 (Connections),
we only provide baseline results using few-shot ICL with LLMs (§3.2).

###  3.1 Task 1: Grouping using Word Embeddings

For the grouping task evaluation (§2.2), we use clustering algorithms on word-
embeddings of the sixteen clue words in each wall, to group them into four
predicted groups that are subsequently evaluated against the four ground-truth
groups for each wall. A vanilla $k$-means (with $k=4$) clustering algorithm
[22] does not guarantee each predicted group to have four words, thus we use
variants like constrained clustering.

#### Clustering

Semi-supervised constrained clustering [65, 5] is used when the user has pre-
existing knowledge about the desired partition (e.g., the partition size of
four). Here, we adopt a minimum cost flow network clustering approach [10]
with a cluster size of four for grouping. Our preliminary analysis showed that
clustering results exhibited slight variations across runs. This slight
discrepancy could be attributed to the initializations of cluster centroids.
To address this issue and ensure reliable results, we report the mean and
variance of results (Table 3)across sixteen (16) runs, each with a unique seed
and randomized order of sixteen-word clues. We tested two additional
clustering approaches motivated by [41, 17]: (1) We constructed a self-
similarity matrix containing pair-wise similar information about the words
prior to applying constrained clustering; (2) We performed dimensionality
reduction using Principal Component Analysis (PCA) [51] and t-distributed
stochastic neighbor embedding (t-SNE) [61] before applying constrained
clustering. Neither approach improved performance over raw embeddings’
clusters, and, for brevity, results are not included.

#### Static word embedding

We used two well-known classic word embedding models, GloVe [44] and FastText
[20], both of which are accessed through the Flair library (Table 2). We used
two FastText models, one pre-trained on the Common Crawl corpus and another on
Wikipedia. Approximately 10% of the total clues encountered in the dataset
were out-of-vocabulary (OOV). A significant portion (~80%) of the OOV cases
were addressed by mean pooling for clues comprised of multiple words to obtain
one unified embedding. For the remaining OOV instances, we combined the static
embeddings with BytePair encoded[23] sub-words.

Figure 3: Solved wall (wall_id="8cde") for Task 1 (Grouping) using best
performing model (E5BASE) with both static and contextual embeddings. Left:
solved wall using static embeddings. Right: unsolved wall using contextual
embeddings. 2D projection of embeddings using t-SNE is shown. Colors and
shapes correspond to true clusters, and grey convex regions correspond to
predicted clusters. The legend shows the ground truth connection for each
group.

#### PLMs

We explored general-purpose PLMs (BERT [15], RoBERTa [35], DistilBERT [49],
ELMo [45]) as well as Sentence Transformers (MPNet [57], E5 [68], see Table
2). We evaluated the performance with and without contextual
embeddings.222Static embeddings are obtained from the PLMs by passing clues
through the model independently. Depending on the context, some clues in the
dataset may appear across different walls with different meanings. As an
example, the word “Gala” was found in three distinct walls, each associated
with a different meaning: apples, swimming ___, and ___ night (Figure 1).
Contextual embeddings could provide valuable semantic information resolving
polysemic ambiguity. The contextual embeddings were generated by joining the
16 clues in the wall as a pseudo-sentence. We randomly shuffle the word order
across sixteen different runs for each wall to account for the positional
ordering. We note that such faux sentences (for inducing context) are not
valid English syntactic sentences. We used mean pooling to generate embeddings
for clues comprised of multiple words to capture the collective meaning of the
entire clue.

Table 2: Details about the baselines and models used in our experiments.

Model |  | # Parameters | Version | Accessed via
---|---|---|---|---
Word Embeddings
BPEmb [23] | En | – | en | Flair [3]
GloVe [44] | 6B | – | glove | Flair
FastText [20] | Crawl | – | crawl | Flair
| News | – | news | Flair
Pre-trained Language Models (PLMs)
ELMoLARGE [45] |  | 94M | large | Flair [3]
DistilBERTBASE [49] | uncased | 66M | distilbert-base-uncased | HuggingFace [75]
BERTBASE [15] | uncased | 110M | bert-base-uncased | HuggingFace
BERTLARGE | uncased | 340M | bert-large-uncased | HuggingFace
RoBERTaLARGE [35] |  | 355M | roberta-large | HuggingFace
Sentence Transformers
all-mpnetBASE [57] | V2 | 110M | sentence-transformers/all-mpnet-base-v2 | HuggingFace
E5BASE [68] | V2 | 110M | intfloat/e5-base-v2 | HuggingFace
E5LARGE | V2 | 340M | intfloat/e5-large-v2 | HuggingFace
Large Language Models (LLM)
GPT-3.5-turbo |  | – | gpt-3.5-turbo-0301 | OpenAI API
GPT-4 |  | – | gpt-4-0314 | OpenAI API

###  3.2 Task 2: Connections using Few-shot In-context Learning (ICL) with
LLMs

Few-shot ICL with LLMs has emerged as a performant and broadly applicable
paradigm in NLP [11]. To evaluate the performance of this approach on our
proposed dataset, we designed a few-shot prompt for GPT-3.5-turbo and GPT-4
[42], which are amongst the strongest performing LLMs currently
available.333In preliminary experiments, we found that open-source LLMs like
LLaMA [60] perform poorly and typically do not follow the task instructions.
For Task 1 (Grouping, §2.2), the prompt consists of some natural language
instructions, several examples of “solved” walls from the training set, and
the current example’s 16 clues, randomly sorted. For Task 2 (Connections), in
place of the 16 clues, the prompt contains a solved wall without the
connections (Figure 4).

Figure 4: Example prompts for Task 1 (Grouping) and Task 2 (Connections) used
with GPT-3.5-turbo and GPT-4. The system’s role includes natural language
instructions. The user role includes $n$ in-context examples and the current
examples 16 clues (Task 1) or the solved wall without connections (Task 2).
For Task 1, the model is instructed to output the solved wall as four lines of
four clues separated by commas. For Task 2, the model is instructed to copy
the solved wall and fill in the connections. Emphasis, underlines and bold
text are for visualization purposes only. Figure 5: Examples of partially and
fully solved walls predicted by GPT-4.

We developed our prompts on the validation set and reported the final
performance on the test set. In-context examples are randomly selected from
the train set; the same examples are used across all test inputs. We
experiment with 0, 1, 3, 5, and 10 in-context examples. When necessary, we
apply simple post-processing to the LLMs output. For example, in both Task 1
and Task 2, we take a maximum of 4 predictions for the groups and connections,
respectively, and pad up to 4 with the empty string in cases where the model
outputs fewer than 4. To make results as reproducible as possible, we set the
temperature=0 and used the 03/01/2023 GPT-3.5-turbo snapshot and the
03/14/2023 GPT-4 snapshot. The max output length is set to 144 tokens. All
other hyperparameters of the OpenAI API are left at their defaults [2].
Prompts were designed as per the Guidance library [1].

##  4 Results and Discussions

###  4.1 Task 1: Grouping Results

#### Embedding Clustering Techniques

Table 3 demonstrates the performance of static and classic embeddings of
selected models on Task 1 (Grouping). E5BASE was the most performant model
and, on average, solved 1 wall and correctly clustered 89 groups. Contextual
embeddings had the lowest overall performance amongst all methods (Table 5 in
Appendix §A). One explanation is that by concatenating all the clues in each
wall, the resulting input may not adhere to the sentence structure that PLMs
are accustomed to during training. This may disrupt the natural flow of
information and, in turn, lead to less meaningful contextual embeddings.
Moreover, the context may change abruptly when combining clues from different
parts of the wall. This can introduce ambiguity and contextual shifts that the
model may struggle to interpret accurately. Another possible explanation is
the effect of positional encoding in the underlying models. Unlike other main
components of PLMs, positional encoding is variant to sequence order [28].
Even though the addition of positional to word embeddings helps with learning
the contextual representation of words at different positions, intrinsic
similarities may be more important than contextual usage. The embedding
dependence on neighboring clues may have hindered the clustering process by
introducing noise and capturing irrelevant information that is specific to a
particular context. For instance, in Figure 3, the contextual embedding model
erroneously associated the clue “Shop” with the connection “Photo ___”,
resulting in the formation of the word “Photoshop”; however, this association
is incorrect as it is an example of a red herring in the wall. In contrast,
the static embedding model correctly mapped “Shop” to its British slang
meaning connection “Betray”. Please refer to Appendix §B for more examples.

#### Few-shot ICL with LLMs

Performance of GPT-4 far surpassed the static (Table 3) and contextual
embedding baselines (Table 5), particularly in terms of the number of solved
walls and correct groups ($>2\text{X}$ the next most performant model, E5),
but was still far below human performance (§4; see §5 for example
predictions). Surprisingly, more in-context examples (from 1 to 10 shots) did
not improve performance. Theories explaining human performance to such tasks
may plausibly extend to this phenomenon. To elucidate, many clues require
open-domain, arcane, cultural (“British”) and quirky knowledge of subject
areas (e.g., “Professional snooker players”, “Female Radio 1 DJs”) that,
without prior memorization, are unlikely to help. The presence of
orthographically similar clue words in examples are red herrings and plausibly
induce negative transfer learning. This is in accordance with ‘red herring
retrieval hypothesis’ factor of ‘repetition’ of incorrect answers further
deteriorating human performance. Another explanation could be the random
sampled in-context examples that are unlikely to be representative of the wide
variety of possible connection types in the dataset. An interesting future
direction would be the evaluation of retrieval augmented models [21, 32, 9,
26], which may be capable of solving groups about highly specific subject
areas. Examining the predictions of the best-performing model (GPT-4, 1-shot),
we found common sources of error to include misformatted outputs (6.1% of all
predicted groups) and hallucinated clues (8.1%).

Table 3: Results of selected models on Task 1 (Grouping) using static
embeddings. WD: Wasserstein Distance. FMS: Fowlkes Mallows Score. ARI:
Adjusted Rand Index. NMI: Normalized Mutual Information. Mean $\pm$ standard
deviation over 16 random seeds is shown. Bold: best scores.

| WD | FMS | ARI | AMI | # Solved Walls | # Correct Groups
---|---|---|---|---|---|---
Classic Embeddings
GloVe | $84.9\pm.4$ | $31.5\pm.3$ | $14.4\pm.3$ | $17.6\pm.4$ | $0\pm 0$ | $68\pm 4$
FastText (Crawl) | $84.2\pm.5$ | $32.1\pm.3$ | $15.2\pm.3$ | $18.4\pm.4$ | $0\pm 0$ | $80\pm 4$
FastText (News) | $85.5\pm.5$ | $30.4\pm.2$ | $13.0\pm.2$ | $15.8\pm.3$ | $0\pm 0$ | $62\pm 3$
Pre-trained Language Models (PLMs)
ELMoLARGE | $86.3\pm.6$ | $29.5\pm.3$ | $11.8\pm.4$ | $14.5\pm.4$ | $0\pm 0$ | $55\pm 4$
DistilBERTBASE | $86.7\pm.6$ | $29.1\pm.2$ | $11.3\pm.3$ | $14.0\pm.3$ | $0\pm 0$ | $49\pm 4$
BERTLARGE | $88.3\pm.5$ | $26.5\pm.2$ |  $8.2\pm.3$ | $10.3\pm.3$ | $0\pm 0$ | $33\pm 2$
BERTBASE | $89.5\pm.4$ | $25.1\pm.2$ |  $6.4\pm.3$ |  $8.1\pm.4$ | $0\pm 0$ | $22\pm 2$
RoBERTaLARGE | $88.4\pm.4$ | $26.7\pm.2$ |  $8.4\pm.3$ |  9.4 $\pm$ .4 | $0\pm 0$ | $29\pm 3$
Sentence Transformers
all-mpnetBASE | $86.3\pm.4$ | $29.4\pm.3$ | $11.7\pm.4$ | $14.3\pm.5$ | $0\pm 0$ | $50\pm 4$
E5LARGE | $84.4\pm.7$ | $32.3\pm.4$ | $15.4\pm.5$ | $18.5\pm.6$ | $0\pm 0$ | $76\pm 5$
E5BASE | $\mathbf{83.8\pm.6}$ | $\mathbf{33.1\pm.3}$ | $\mathbf{16.3\pm.4}$ | $\mathbf{19.5\pm.4}$ | $\mathbf{1\pm 0}$ | $\mathbf{89\pm 6}$
Human Performance | – | – | – | – |  $285$ / $494$ |  $1405$ / $1976$

Table 4: Results on Task 1 (Grouping) using Large Language Models. WD:
Wasserstein Distance. FMS: Fowlkes Mallows Score. ARI: Adjusted Rand Index.
NMI: Normalized Mutual Information. Bold: best scores.

| # In-context Examples | WD | FMS | ARI | AMI | # Solved Walls | # Correct Groups
---|---|---|---|---|---|---|---
GPT-3.5-turbo | 0-shot | $93.5$ | $16.7$ |  $8.8$ | $10.4$ | $0$ |  $47$
| 1-shot | $85.0$ | $32.5$ | $17.2$ | $20.1$ | $1$ | $105$
| 3-shot | $81.6$ | $35.9$ | $20.2$ | $23.4$ | $1$ | $143$
| 5-shot | $81.9$ | $36.4$ | $20.8$ | $24.1$ | $1$ | $141$
| 10-shot | $82.5$ | $35.5$ | $19.8$ | $22.9$ | $1$ | $132$
GPT-4 | 0-shot | $73.9$ | $43.2$ | $\mathbf{29.1}$ | $\mathbf{32.9}$ | $5$ | $258$
| 1-shot | $\mathbf{73.0}$ | $\mathbf{43.3}$ | $\mathbf{29.1}$ | $32.7$ | $\mathbf{7}$ | $\mathbf{268}$
| 3-shot | $74.7$ | $42.7$ | $28.4$ | $32.0$ | $6$ | $246$
| 5-shot | $73.9$ | $42.8$ | $28.5$ | $32.2$ | $5$ | $248$
| 10-shot | $74.9$ | $41.6$ | $27.0$ | $30.6$ | $4$ | $238$
Human Performance |  | – | – | – | – |  $285$ / $494$ |  $1405$ / $1976$

###  4.2 Task 2: Connections Results

Figure 6: Results for Task 2 (Connections) with GPT-3.5-turbo and GPT-4. For
reference, human performance is approximately 80% (fraction of correctly
answered connections). We report $\max(\text{BERTScore},0)$ in the case of
GPT-3.5-turbo for readability.

In Figure 6, we present the results for Task 2 (Connections). In general,
GPT-4 outperforms GPT-3.5-turbo, especially in the 0-shot regime. Performance
for GPT-4 improves monotonically with an increasing number of in-context
examples, although improvements are sometimes small (e.g., $<0.01$). As
expected, the exact match score for both models is low ($<15\%$). This is
explained by the fact that even insignificant differences between the model’s
predictions and the ground truth will result in a score of 0 (e.g., “Made of
rubber” vs. “Made from rubber”). For this reason, we also report ROUGE-1 and
BERTScore F1 scores (§2.2). Although not a perfect comparison, we can
contextualize these results with human performance, which we recorded as the
fraction of correctly guessed connections: $\sim$80% on the test set. The quiz
show Only Connect allows for some small deviations in guessed connections that
will be accepted as correct, making the comparison to ROUGE and BERTScore more
suitable than to exact match. Our results suggest that at 41-45% F1, the best
performance achieved with few-shot ICL (GPT-4, 10-shot) is far below human
performance. Lastly, we note that a common source of model error was the
inclusion of clues in the predicted connection (occurring in 8.2% of all
predicted connections for the best performing model), e.g., “Fireplace tools
(Spade, Brush, Poker, Tongs)”, even though (1) the model was not instructed to
do so, and (2) the in-context examples were not formatted like this. More
complicated post-processing or prompting strategies (e.g., “Tree of Thoughts”
[78]) could mitigate these issues and improve performance. We leave this for
future work.

##  5 Related Work

Various datasets and tasks have been proposed for evaluating language models
against human-like linguistic capabilities. Earlier examples of such tasks
include word sense disambiguation (WSD) [48], Winograd schema challenge [30]
and word sense induction (WSI) [72]. WSD aims to determine a word’s correct
meaning or sense within a specific context. WSI focuses on automatically
clustering words into different senses or semantic categories based on their
contextual usage patterns. Benchmarks like GLUE [67] and SuperGLUE [66] are
aimed at aggregating and standardizing these classical NLP tasks to evaluate
language models. The PLMs (e.g., BERT variants) and the first generation of
LLMs, mostly solved or attained human-level performance on these tasks by
2020s [36].

In order to evaluate the human-imitative capabilities of modern LLMs, more
challenging tasks have been proposed in recent benchmarks like BIG-bench [59]
and HumanEval [13]. BIG-bench aims to address the limitations of existing
benchmarks by providing a more comprehensive, open, and dynamic (tasks added
on a rolling basis) evaluation benchmark. It covers a wide range of tasks,
including a suite of tasks targeted specifically for human-like behaviour.
HumanEval is an evaluation set to measure the functional correctness of code
synthesis from docstrings [13]. This benchmark includes 164 original
programming problems that assess language comprehension, algorithms, and
simple mathematics comparable to simple software interview questions. While
these recent benchmarks include a wide net of complex tasks, evaluating a
broad range of LLM capabilities, our work here is orthogonal to these since
none of them aims to specifically measure creative problem solving or
creativity and their impediments in LLMs.

##  6 Limitations & Future Work

As with any machine learning dataset, especially one designed to evaluate the
performance of LLMs, the OCW dataset has several limitations. First, we
noticed that the performance of contextual approaches can vary significantly
depending on the order that clues are provided to the model. To alleviate this
(and where feasible), we evaluate models across 16 random sortings of the
clues. Due to cost, we did not evaluate GPT-3.5-turbo and GPT-4’s sensitivity
to this ordering; future work should report performance across multiple random
sorts. Second, due to the nature of the quiz show Only Connect, the clues,
groups, and connections in the dataset tend to be Western- (and specifically
UK-) centric (e.g. “US presidential losers”, “English cricket captains”,
“Irish counties”). Therefore, performance on the OCW dataset may not
extrapolate to language or subcultures outside of Western English. We hope to
add additional Only Connect inspired walls in multiple languages and with
clues derived from various subcultures in future work.

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##  Appendix A Additional Experiments

#### Task 1 – Grouping

In addition to grouping clue words using token embeddings (discussed in the
main paper §4), we also ran grouping the words by clustering on ‘contextual’
embeddings. We experimentally induce ‘context’ by joining the sixteen (16)
word tokens (in a random order) into a single pseudo-sentence. The embeddings
for each token were different based on the ordering of the tokens. We repeat
the random ordering sixteen times and report the mean and variance of the
results obtained in Table 5.

Table 5: Results of selected models on Task 1 (Grouping) using contextual
embeddings. WD: Wasserstein Distance. FMS: Fowlkes Mallows Score. ARI:
Adjusted Rand Index. NMI: Normalized Mutual Information. Mean $\pm$ standard
deviation over 16 random seeds is shown. Bold: best scores.

| WD | FMS | ARI | AMI | # Solved Walls | # Correct Groups
---|---|---|---|---|---|---
ELMoLARGE | $90.0\pm.3$ | $23.6\pm.4$ |  $4.5\pm.5$ |  $5.6\pm.7$ | $0\pm 0$ | $19\pm 3$
DistilBERTBASE | $88.4\pm.7$ | $26.7\pm.3$ |  $8.3\pm.4$ | $10.4\pm.5$ | $0\pm 0$ | $30\pm 4$
BERTLARGE | $\mathbf{87.2\pm.6}$ | $\mathbf{28.3\pm.5}$ | $\mathbf{10.4\pm.6}$ | $\mathbf{12.8\pm.7}$ | $0\pm 0$ | $\mathbf{46\pm 5}$
BERTBASE | $87.7\pm.5$ | $28.0\pm.2$ | $10.0\pm.3$ | $12.4\pm.4$ | $0\pm 0$ | $39\pm 2$
RoBERTaLARGE | $88.4\pm.5$ | $25.9\pm.2$ |  $7.4\pm.3$ |  $9.3\pm.4$ | $0\pm 0$ | $30\pm 4$
all-mpnetBASE | $87.6\pm.5$ | $28.0\pm.3$ | $10.0\pm.4$ | $12.4\pm.5$ | $0\pm 0$ | $38\pm 3$
E5LARGE | $87.7\pm.5$ | $28.1\pm.3$ | $10.2\pm.4$ | $12.7\pm.5$ | $0\pm 0$ | $37\pm 4$
E5BASE | $\mathbf{87.2\pm.3}$ | $28.2\pm.2$ | $10.2\pm.3$ | $12.5\pm.4$ | $0\pm 0$ | $\mathbf{46\pm 5}$
Human Performace | – | – | – | – |  $285$ / $494$ |  $1405$ / $1976$

#### Task 2 – Connections

In addition to prompting based results on GPT-4 (discussed in §4), we ran
experiments on additional LLMs like LLaMa [60] (7B, 13B) using pre-trained
configuration weights obtained by permission from Meta AI. However, without
additional fine-tuning on the specific task, these LLMs were unable to solve
the task in a meaningful manner. To elucidate, LLaMa generated a bunch of
hallucinated words with unequal group sizes. We omit these unintelligible
results for brevity.

##  Appendix B Additional Figures

In this section, we provide additional t-SNE projections of embeddings from
various methods used.

Figure 7: Solved wall for Task 1 (Grouping) using GloVe. Left:
(wall_id="7ed3"), the embedding model erroneously associated the clue
“Suspension” with the connection “Bridges”; however, this association is an
example of a red herring. “Suspension” is “a term used in musical harmony” in
this context. Right: (wall_id="5e3c"), shows that clue “Lord” is close to
“God, Heavens, and Grief” in the embedding space, which matches the “Good
___!” connection. However, this is another example of a red herring as, in
this context, “Lord” refers to “Lord’s cricket Ground”, a cricket stadium
named after “Thomas Lord”.

Figure 8: Solved wall for Task 1 (Grouping) using FastText (Crawl). Left:
(wall_id="d5e6"), the embedding model erroneously associated the clue “Tara”
other girls’ names; but here, “Tara” is short for “Hill of Tara” and belongs
to the “national coronation sites” group. Right: (wall_id="4c22"), shows that
clue “Pie” associated with the connection “Apple”. Even though it is
acceptable in general context, here it represents a homophone for the Greek
letter “$\pi$”.

Figure 9: Solved wall (wall_id="2d8f") for Task 1 (Grouping) using BERTLARGE
with both static and contextual embeddings. Left: contextual embedding solved
3/4 groups. Here the clue “Rambrandt” is placed near other Dutch painters. The
correct grouping for this clue in this wall is “Toothpaste Brands”. Right:
static embedding solved 0/4 groups.