QCRI/LlamaLens-Arabic-Native

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.

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

Arabic Datasets

Task	Dataset	# Labels	# Train	# Test	# Dev
Attentionworthiness	CT22Attentionworthy	9	2,470	1,186	1,071
Checkworthiness	CT24_T1	2	22,403	500	1,093
Claim	CT22Claim	2	3,513	1,248	339
Cyberbullying	ArCyc_CB	2	3,145	900	451
Emotion	Emotional-Tone	8	7,024	2,009	1,005
Emotion	NewsHeadline	7	939	323	160
Factuality	Arafacts	5	4,354	1,245	623
Factuality	COVID19Factuality	2	3,513	988	339
Harmful	CT22Harmful	2	2,484	1,201	1,076
Hate Speech	annotated-hatetweets-4-classes	4	210,526	100,565	90,544
Hate Speech	OSACT4SubtaskB	2	4,778	1,827	2,048
News Genre Categorization	ASND	10	74,496	21,942	11,136
News Genre Categorization	SANADAkhbarona	7	62,210	7,824	7,824
News Genre Categorization	SANADAlArabiya	6	56,967	7,123	7,120
News Genre Categorization	SANADAlkhaleej	7	36,391	4,550	4,550
News Genre Categorization	UltimateDataset	10	133,036	38,456	19,269
News Credibility	NewsCredibilityDataset	2	8,671	2,730	1,426
Summarization	xlsum	--	37,425	4,689	4,689
Offensive Language	ArCyc_OFF	2	3,138	900	450
Offensive Language	OSACT4SubtaskA	2	4,780	1,827	2,047
Propaganda	ArPro	2	6,002	1,326	672
Sarcasm	ArSarcasm-v2	2	8,749	2,996	3,761
Sentiment	ar_reviews_100k	3	69,998	20,000	10,000
Sentiment	ArSAS	4	13,883	3,976	1,987
Stance	Mawqif-Arabic-Stance-main	2	3,162	560	950
Stance	stance	3	2,652	379	755
Subjectivity	ThatiAR	2	2,446	748	467

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)
Attentionworthiness Detection	CT22Attentionworthy	W-F1	0.412	0.158	0.425	0.454	0.013
Checkworthiness Detection	CT24_checkworthy	F1_Pos	0.569	0.610	0.502	0.509	-0.067
Claim Detection	CT22Claim	Acc	0.703	0.581	0.734	0.756	0.031
Cyberbullying Detection	ArCyc_CB	Acc	0.863	0.766	0.870	0.833	0.007
Emotion Detection	Emotional-Tone	W-F1	0.658	0.358	0.705	0.736	0.047
Emotion Detection	NewsHeadline	Acc	1.000	0.406	0.480	0.458	-0.520
Factuality	Arafacts	Mi-F1	0.850	0.210	0.771	0.738	-0.079
Factuality	COVID19Factuality	W-F1	0.831	0.492	0.800	0.840	-0.031
Harmfulness Detection	CT22Harmful	F1_Pos	0.557	0.507	0.523	0.535	-0.034
Hate Speech Detection	annotated-hatetweets-4-classes	W-F1	0.630	0.257	0.526	0.517	-0.104
Hate Speech Detection	OSACT4SubtaskB	Mi-F1	0.950	0.819	0.955	0.955	0.005
News Categorization	ASND	Ma-F1	0.770	0.587	0.919	0.929	0.149
News Categorization	SANADAkhbarona-news-categorization	Acc	0.940	0.784	0.954	0.953	0.014
News Categorization	SANADAlArabiya-news-categorization	Acc	0.974	0.893	0.987	0.985	0.013
News Categorization	SANADAlkhaleej-news-categorization	Acc	0.986	0.865	0.984	0.982	-0.002
News Categorization	UltimateDataset	Ma-F1	0.970	0.376	0.865	0.880	-0.105
News Credibility	NewsCredibilityDataset	Acc	0.899	0.455	0.935	0.933	0.036
News Summarization	xlsum	R-2	0.137	0.034	0.129	0.130	-0.009
Offensive Language Detection	ArCyc_OFF	Ma-F1	0.878	0.489	0.877	0.879	-0.001
Offensive Language Detection	OSACT4SubtaskA	Ma-F1	0.905	0.782	0.896	0.882	-0.009
Propaganda Detection	ArPro	Mi-F1	0.767	0.597	0.747	0.731	-0.020
Sarcasm Detection	ArSarcasm-v2	F1_Pos	0.584	0.477	0.520	0.542	-0.064
Sentiment Classification	ar_reviews_100k	F1_Pos	--	0.681	0.785	0.779	--
Sentiment Classification	ArSAS	Acc	0.920	0.603	0.800	0.804	-0.120
Stance Detection	stance	Ma-F1	0.767	0.608	0.926	0.881	0.159
Stance Detection	Mawqif-Arabic-Stance-main	Ma-F1	0.789	0.764	0.853	0.826	0.065
Subjectivity Detection	ThatiAR	f1_pos	0.800	0.562	0.441	0.383	-0.359

---

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": "c64503bb-9253-4f58-aef8-9b244c088b15",
    "original_id": "1,722,643,241,323,950,300",
    "input": "يريدون توريط السلطة الفلسطينية في الضفة ودق آخر مسمار في نعش ما تبقى من هويتنا الفلسطينية، كما تم توريط غزة. يريدون إعلان كفاح مسلح من طرف الأجهزة الأمنية الفلسطينية علناً! لكن ما يعلمونه وما يرونه ولا يريدون التحدث به، أن أبناء الأجهزة الأمنية في النهار يكونون عسكريين... https://t.co/qF2Fjh24hV https://t.co/1UicLkDd52",
    "output": "checkworthy",
    "dataset": "Checkworthiness",
    "task": "Checkworthiness",
    "lang": "ar",
    "instructions": "Identify if the given factual claim is 'checkworthy' or 'not-checkworthy'. Return only the label without any explanation, justification, or additional text."
}

Model

LlamaLens on Hugging Face

Replication Scripts

LlamaLens GitHub Repository

📢 Citation

If you use this dataset, please cite our paper:

@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}
}