---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- Social Media
- News Media
- Sentiment
- Stance
- Emotion
pretty_name: >-
  LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media
  Content -- English
size_categories:
- 10K<n<100K
dataset_info:
- config_name: QProp
  splits:
  - name: train
    num_examples: 35986
  - name: dev
    num_examples: 5125
  - name: test
    num_examples: 10159
- config_name: Cyberbullying
  splits:
  - name: train
    num_examples: 32551
  - name: dev
    num_examples: 4751
  - name: test
    num_examples: 9473
- config_name: clef2024-checkthat-lab
  splits:
  - name: train
    num_examples: 825
  - name: dev
    num_examples: 219
  - name: test
    num_examples: 484
- config_name: SemEval23T3-subtask1
  splits:
  - name: train
    num_examples: 302
  - name: dev
    num_examples: 130
  - name: test
    num_examples: 83
- config_name: offensive_language_dataset
  splits:
  - name: train
    num_examples: 29216
  - name: dev
    num_examples: 3653
  - name: test
    num_examples: 3653
- config_name: xlsum
  splits:
  - name: train
    num_examples: 306493
  - name: dev
    num_examples: 11535
  - name: test
    num_examples: 11535
- config_name: claim-detection
  splits:
  - name: train
    num_examples: 23224
  - name: dev
    num_examples: 5815
  - name: test
    num_examples: 7267
- config_name: emotion
  splits:
  - name: train
    num_examples: 280551
  - name: dev
    num_examples: 41429
  - name: test
    num_examples: 82454
- config_name: Politifact
  splits:
  - name: train
    num_examples: 14799
  - name: dev
    num_examples: 2116
  - name: test
    num_examples: 4230
- config_name: News_dataset
  splits:
  - name: train
    num_examples: 28147
  - name: dev
    num_examples: 4376
  - name: test
    num_examples: 8616
- config_name: hate-offensive-speech
  splits:
  - name: train
    num_examples: 48944
  - name: dev
    num_examples: 2802
  - name: test
    num_examples: 2799
- config_name: CNN_News_Articles_2011-2022
  splits:
  - name: train
    num_examples: 32193
  - name: dev
    num_examples: 9663
  - name: test
    num_examples: 5682
- config_name: CT24_checkworthy
  splits:
  - name: train
    num_examples: 22403
  - name: dev
    num_examples: 318
  - name: test
    num_examples: 1031
- config_name: News_Category_Dataset
  splits:
  - name: train
    num_examples: 145748
  - name: dev
    num_examples: 20899
  - name: test
    num_examples: 41740
- config_name: NewsMTSC-dataset
  splits:
  - name: train
    num_examples: 7739
  - name: dev
    num_examples: 320
  - name: test
    num_examples: 747
- config_name: Offensive_Hateful_Dataset_New
  splits:
  - name: train
    num_examples: 42000
  - name: dev
    num_examples: 5254
  - name: test
    num_examples: 5252
- config_name: News-Headlines-Dataset-For-Sarcasm-Detection
  splits:
  - name: train
    num_examples: 19965
  - name: dev
    num_examples: 2858
  - name: test
    num_examples: 5719
configs:
- config_name: QProp
  data_files:
  - split: test
    path: QProp/test.json
  - split: dev
    path: QProp/dev.json
  - split: train
    path: QProp/train.json
- config_name: Cyberbullying
  data_files:
  - split: test
    path: Cyberbullying/test.json
  - split: dev
    path: Cyberbullying/dev.json
  - split: train
    path: Cyberbullying/train.json
- config_name: clef2024-checkthat-lab
  data_files:
  - split: test
    path: clef2024-checkthat-lab/test.json
  - split: dev
    path: clef2024-checkthat-lab/dev.json
  - split: train
    path: clef2024-checkthat-lab/train.json
- config_name: SemEval23T3-subtask1
  data_files:
  - split: test
    path: SemEval23T3-subtask1/test.json
  - split: dev
    path: SemEval23T3-subtask1/dev.json
  - split: train
    path: SemEval23T3-subtask1/train.json
- config_name: offensive_language_dataset
  data_files:
  - split: test
    path: offensive_language_dataset/test.json
  - split: dev
    path: offensive_language_dataset/dev.json
  - split: train
    path: offensive_language_dataset/train.json
- config_name: xlsum
  data_files:
  - split: test
    path: xlsum/test.json
  - split: dev
    path: xlsum/dev.json
  - split: train
    path: xlsum/train.json
- config_name: claim-detection
  data_files:
  - split: test
    path: claim-detection/test.json
  - split: dev
    path: claim-detection/dev.json
  - split: train
    path: claim-detection/train.json
- config_name: emotion
  data_files:
  - split: test
    path: emotion/test.json
  - split: dev
    path: emotion/dev.json
  - split: train
    path: emotion/train.json
- config_name: Politifact
  data_files:
  - split: test
    path: Politifact/test.json
  - split: dev
    path: Politifact/dev.json
  - split: train
    path: Politifact/train.json
- config_name: News_dataset
  data_files:
  - split: test
    path: News_dataset/test.json
  - split: dev
    path: News_dataset/dev.json
  - split: train
    path: News_dataset/train.json
- config_name: hate-offensive-speech
  data_files:
  - split: test
    path: hate-offensive-speech/test.json
  - split: dev
    path: hate-offensive-speech/dev.json
  - split: train
    path: hate-offensive-speech/train.json
- config_name: CNN_News_Articles_2011-2022
  data_files:
  - split: test
    path: CNN_News_Articles_2011-2022/test.json
  - split: dev
    path: CNN_News_Articles_2011-2022/dev.json
  - split: train
    path: CNN_News_Articles_2011-2022/train.json
- config_name: CT24_checkworthy
  data_files:
  - split: test
    path: CT24_checkworthy/test.json
  - split: dev
    path: CT24_checkworthy/dev.json
  - split: train
    path: CT24_checkworthy/train.json
- config_name: News_Category_Dataset
  data_files:
  - split: test
    path: News_Category_Dataset/test.json
  - split: dev
    path: News_Category_Dataset/dev.json
  - split: train
    path: News_Category_Dataset/train.json
- config_name: NewsMTSC-dataset
  data_files:
  - split: test
    path: NewsMTSC-dataset/test.json
  - split: dev
    path: NewsMTSC-dataset/dev.json
  - split: train
    path: NewsMTSC-dataset/train.json
- config_name: Offensive_Hateful_Dataset_New
  data_files:
  - split: test
    path: Offensive_Hateful_Dataset_New/test.json
  - split: dev
    path: Offensive_Hateful_Dataset_New/dev.json
  - split: train
    path: Offensive_Hateful_Dataset_New/train.json
- config_name: News-Headlines-Dataset-For-Sarcasm-Detection
  data_files:
  - split: test
    path: News-Headlines-Dataset-For-Sarcasm-Detection/test.json
  - split: dev
    path: News-Headlines-Dataset-For-Sarcasm-Detection/dev.json
  - split: train
    path: News-Headlines-Dataset-For-Sarcasm-Detection/train.json
---

# LlamaLens: Specialized Multilingual LLM Dataset

## Overview
LlamaLens is a specialized multilingual LLM designed for analyzing news and social media content. It focuses on 19 NLP tasks, leveraging 52 datasets across Arabic, English, and Hindi.


<p align="center"> <img src="./capablities_tasks_datasets.png" style="width: 40%;" id="title-icon"> </p>

## LlamaLens
This repo includes scripts needed to run our full pipeline, including data preprocessing and sampling, instruction dataset creation, model fine-tuning, inference and evaluation.

### Features
- Multilingual support (Arabic, English, Hindi)
- 19 NLP tasks with 52 datasets
- Optimized for news and social media content analysis

## 📂 Dataset Overview

### English Datasets

| **Task**                  | **Dataset**                  | **# Labels** | **# Train** | **# Test** | **# Dev** |
|---------------------------|------------------------------|--------------|-------------|------------|-----------|
| Checkworthiness           | CT24_T1                      | 2            | 22,403      | 1,031      | 318       |
| Claim                    | claim-detection              | 2            | 23,224      | 7,267      | 5,815     |
| Cyberbullying             | Cyberbullying                | 6            | 32,551      | 9,473      | 4,751     |
| Emotion                  | emotion                     | 6            | 280,551     | 82,454     | 41,429    |
| Factuality               | News_dataset                | 2            | 28,147      | 8,616      | 4,376     |
| Factuality               | Politifact                  | 6            | 14,799      | 4,230      | 2,116     |
| News Genre Categorization | CNN_News_Articles_2011-2022 | 6            | 32,193      | 5,682      | 9,663     |
| News Genre Categorization | News_Category_Dataset       | 42           | 145,748     | 41,740     | 20,899    |
| News Genre Categorization | SemEval23T3-subtask1        | 3            | 302         | 83         | 130       |
| Summarization            | xlsum                       | --           | 306,493     | 11,535     | 11,535    |
| Offensive Language       | Offensive_Hateful_Dataset_New | 2           | 42,000      | 5,252      | 5,254     |
| Offensive Language       | offensive_language_dataset   | 2            | 29,216      | 3,653      | 3,653     |
| Offensive/Hate-Speech    | hate-offensive-speech        | 3            | 48,944      | 2,799      | 2,802     |
| Propaganda               | QProp                        | 2            | 35,986      | 10,159     | 5,125     |
| Sarcasm                  | News-Headlines-Dataset-For-Sarcasm-Detection | 2 | 19,965 | 5,719 | 2,858 |
| Sentiment                | NewsMTSC-dataset            | 3            | 7,739       | 747        | 320       |
| Subjectivity             | clef2024-checkthat-lab       | 2            | 825         | 484        | 219       |


## Results

Below, we present the performance of **LlamaLens** in **English** compared to existing SOTA (if available) and the Llama-Instruct baseline, The “Δ” (Delta) column here is 
calculated as **(LLamalens – SOTA)**.

| **Task**             | **Dataset**               | **Metric** | **SOTA** | **Llama-instruct** | **LLamalens** | **Δ** (LLamalens - SOTA) |
|----------------------|---------------------------|-----------:|--------:|--------------------:|--------------:|------------------------------:|
| News Summarization   | xlsum                     | R-2        | 0.152   | 0.074              | 0.141         | -0.011                       |
| News Genre           | CNN_News_Articles         | Acc        | 0.940   | 0.644              | 0.915         | -0.025                       |
| News Genre           | News_Category             | Ma-F1      | 0.769   | 0.970              | 0.505         | -0.264                       |
| News Genre           | SemEval23T3-ST1           | Mi-F1      | 0.815   | 0.687              | 0.241         | -0.574                       |
| Subjectivity         | CT24_T2                   | Ma-F1      | 0.744   | 0.535              | 0.508         | -0.236                       |
| Emotion              | emotion                   | Ma-F1      | 0.790   | 0.353              | 0.878         | 0.088                        |
| Sarcasm              | News-Headlines            | Acc        | 0.897   | 0.668              | 0.956         | 0.059                        |
| Sentiment            | NewsMTSC                  | Ma-F1      | 0.817   | 0.628              | 0.627         | -0.190                       |
| Checkworthiness      | CT24_T1                   | F1_Pos     | 0.753   | 0.404              | 0.877         | 0.124                        |
| Claim                | claim-detection           | Mi-F1      | –       | 0.545              | 0.915         | –                            |
| Factuality           | News_dataset              | Acc        | 0.920   | 0.654              | 0.946         | 0.026                        |
| Factuality           | Politifact                | W-F1       | 0.490   | 0.121              | 0.290         | -0.200                       |
| Propaganda           | QProp                     | Ma-F1      | 0.667   | 0.759              | 0.851         | 0.184                        |
| Cyberbullying        | Cyberbullying             | Acc        | 0.907   | 0.175              | 0.847         | -0.060                       |
| Offensive            | Offensive_Hateful         | Mi-F1      | –       | 0.692              | 0.805         | –                            |
| Offensive            | offensive_language        | Mi-F1      | 0.994   | 0.646              | 0.884         | -0.110                       |
| Offensive & Hate     | hate-offensive-speech     | Acc        | 0.945   | 0.602              | 0.924         | -0.021                       |


## File Format

Each JSONL file in the dataset follows a structured format with the following fields:

- `id`: Unique identifier for each data entry.
- `original_id`: Identifier from the original dataset, if available.
- `input`: The original text that needs to be analyzed.
- `output`: The label assigned to the text after analysis.
- `dataset`: Name of the dataset the entry belongs.
- `task`: The specific task type.
- `lang`: The language of the input text.
- `instructions`: A brief set of instructions describing how the text should be labeled.
- `text`: A formatted structure including instructions and response for the task in a conversation format between the system, user, and assistant, showing the decision process.


**Example entry in JSONL file:**

```
{
    "id": "3fe3eb6a-843e-4a03-b38c-8333c052f4c4",
    "original_id": "nan",
    "input": "You know, I saw a movie - \"Crocodile Dundee.\"",
    "output": "not_checkworthy",
    "dataset": "CT24_checkworthy",
    "task": "Checkworthiness",
    "lang": "en",
    "instructions": "Analyze the given text and label it as 'checkworthy' if it includes a factual statement that is significant or relevant to verify, or 'not_checkworthy' if it's not worth checking. Return only the label without any explanation, justification or additional text.",
    "text": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are a social media expert providing accurate analysis and insights.<|eot_id|><|start_header_id|>user<|end_header_id|>Analyze the given text and label it as 'checkworthy' if it includes a factual statement that is significant or relevant to verify, or 'not_checkworthy' if it's not worth checking. Return only the label without any explanation, justification or additional text.\ninput: You know, I saw a movie - \"Crocodile Dundee.\"\nlabel: <|eot_id|><|start_header_id|>assistant<|end_header_id|>not_checkworthy<|eot_id|><|end_of_text|>"
}

```
## Model  
[**LlamaLens on Hugging Face**](https://huggingface.co/QCRI/LlamaLens)  

## Replication Scripts  
[**LlamaLens GitHub Repository**](https://github.com/firojalam/LlamaLens)


## 📢 Citation

If you use this dataset, please cite our [paper](https://arxiv.org/pdf/2410.15308):

```
@article{kmainasi2024llamalensspecializedmultilingualllm,
  title={LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content},
  author={Mohamed Bayan Kmainasi and Ali Ezzat Shahroor and Maram Hasanain and Sahinur Rahman Laskar and Naeemul Hassan and Firoj Alam},
  year={2024},
  journal={arXiv preprint arXiv:2410.15308},
  volume={},
  number={},
  pages={},
  url={https://arxiv.org/abs/2410.15308},
  eprint={2410.15308},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
```