--- license: cc-by-nc-sa-4.0 task_categories: - text-classification language: - ar tags: - Social Media - News Media - Sentiment - Stance - Emotion pretty_name: 'LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content -- Arabic' size_categories: - 10K

## 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**](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} } ```