LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917938218401792 |
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| author | Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Hasanain, Maram Laskar, Sahinur Rahman Hassan, Naeemul Alam, Firoj |
| author_facet | Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Hasanain, Maram Laskar, Sahinur Rahman Hassan, Naeemul Alam, Firoj |
| contents | Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. However, their capabilities remain limited when addressing domain-specific problems, particularly in downstream NLP tasks. Research has shown that models fine-tuned on instruction-based downstream NLP datasets outperform those that are not fine-tuned. While most efforts in this area have primarily focused on resource-rich languages like English and broad domains, little attention has been given to multilingual settings and specific domains. To address this gap, this study focuses on developing a specialized LLM, LlamaLens, for analyzing news and social media content in a multilingual context. To the best of our knowledge, this is the first attempt to tackle both domain specificity and multilinguality, with a particular focus on news and social media. Our experimental setup includes 18 tasks, represented by 52 datasets covering Arabic, English, and Hindi. We demonstrate that LlamaLens outperforms the current state-of-the-art (SOTA) on 23 testing sets, and achieves comparable performance on 8 sets. We make the models and resources publicly available for the research community (https://huggingface.co/collections/QCRI/llamalens-672f7e0604a0498c6a2f0fe9). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_15308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Hasanain, Maram Laskar, Sahinur Rahman Hassan, Naeemul Alam, Firoj Computation and Language Artificial Intelligence 68T50 F.2.2; I.2.7 Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. However, their capabilities remain limited when addressing domain-specific problems, particularly in downstream NLP tasks. Research has shown that models fine-tuned on instruction-based downstream NLP datasets outperform those that are not fine-tuned. While most efforts in this area have primarily focused on resource-rich languages like English and broad domains, little attention has been given to multilingual settings and specific domains. To address this gap, this study focuses on developing a specialized LLM, LlamaLens, for analyzing news and social media content in a multilingual context. To the best of our knowledge, this is the first attempt to tackle both domain specificity and multilinguality, with a particular focus on news and social media. Our experimental setup includes 18 tasks, represented by 52 datasets covering Arabic, English, and Hindi. We demonstrate that LlamaLens outperforms the current state-of-the-art (SOTA) on 23 testing sets, and achieves comparable performance on 8 sets. We make the models and resources publicly available for the research community (https://huggingface.co/collections/QCRI/llamalens-672f7e0604a0498c6a2f0fe9). |
| title | LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content |
| topic | Computation and Language Artificial Intelligence 68T50 F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2410.15308 |