---
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 18 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)
- 18 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 **L-Lens: LlamaLens** , where *"Eng"* refers to the English-instructed model and *"Native"* refers to the model trained with native language instructions. The results are compared against the SOTA (where available) and the Base: **Llama-Instruct 3.1 baseline**. The **Δ** (Delta) column indicates the difference between LlamaLens and the SOTA performance, calculated as (LlamaLens – SOTA).


|              **Task**              |                  **Dataset**                 | **Metric** | **SOTA** | **Base** | **L-Lens-Eng** | **L-Lens-Native** | **Δ (L-Lens (Eng) - SOTA)** |
|:----------------------------------:|:--------------------------------------------:|:----------:|:--------:|:---------------------:|:---------------------:|:--------------------:|:------------------------:|
|      Checkworthiness Detection     |               CT24_checkworthy               |   f1_pos   |   0.753  |         0.404         |         0.942         |         0.942        |           0.189          |
|           Claim Detection          |                claim-detection               |    Mi-F1   |    --    |         0.545         |         0.864         |         0.889        |            --            |
|       Cyberbullying Detection      |                 Cyberbullying                |     Acc    |   0.907  |         0.175         |         0.836         |         0.855        |          -0.071          |
|         Emotion Detection          |                    emotion                   |    Ma-F1   |   0.790  |         0.353         |         0.803         |         0.808        |           0.013          |
|             Factuality             |                 News_dataset                 |     Acc    |   0.920  |         0.654         |         1.000         |         1.000        |           0.080          |
|             Factuality             |                  Politifact                  |    W-F1    |   0.490  |         0.121         |         0.287         |         0.311        |          -0.203          |
|         News Categorization        |          CNN_News_Articles_2011-2022         |     Acc    |   0.940  |         0.644         |         0.970         |         0.970        |           0.030          |
|         News Categorization        |             News_Category_Dataset            |    Ma-F1   |   0.769  |         0.970         |         0.824         |         0.520        |           0.055          |
|      News Genre Categorisation     |             SemEval23T3-subtask1             |    Mi-F1   |   0.815  |         0.687         |         0.241         |         0.253        |          -0.574          |
|         News Summarization         |                     xlsum                    |     R-2    |   0.152  |         0.074         |         0.182         |         0.181        |           0.030          |
|    Offensive Language Detection    |         Offensive_Hateful_Dataset_New        |    Mi-F1   |    --    |         0.692         |         0.814         |         0.813        |            --            |
|    Offensive Language Detection    |          offensive_language_dataset          |    Mi-F1   |   0.994  |         0.646         |         0.899         |         0.893        |          -0.095          |
| Offensive Language and Hate Speech |             hate-offensive-speech            |     Acc    |   0.945  |         0.602         |         0.931         |         0.935        |          -0.014          |
|        Propaganda Detection        |                     QProp                    |    Ma-F1   |   0.667  |         0.759         |         0.963         |         0.973        |           0.296          |
|          Sarcasm Detection         | News-Headlines-Dataset-For-Sarcasm-Detection |     Acc    |   0.897  |         0.668         |         0.936         |         0.947        |           0.039          |
|      Sentiment Classification      |               NewsMTSC-dataset               |    Ma-F1   |   0.817  |         0.628         |         0.751         |         0.748        |          -0.066          |
|       Subjectivity Detection       |            clef2024-checkthat-lab            |    Ma-F1   |   0.744  |         0.535         |         0.642         |         0.628        |          -0.102          |
|

---



## 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.


**Example entry in JSONL file:**

```
{
    "id": "fb6dd1bb-2ab4-4402-adaa-9be9eea6ca18",
    "original_id": null,
    "input": "I feel that worldviews that lack the divine tend toward the solipsistic.",
    "output": "joy",
    "dataset": "Emotion",
    "task": "Emotion",
    "lang": "en",
    "instructions": "Identify if the given text expresses an emotion and specify whether it is joy, love, fear, anger, sadness, or surprise. Return only the label without any explanation, justification, or additional 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}
}
```