LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content

• URL: http://arxiv.org/abs/2410.15308v2
• Date: Thu, 27 Feb 2025 07:01:29 GMT
• Title: LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
• Authors: Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar, Naeemul Hassan, Firoj Alam,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields.<n>This study focuses on developing a specialized LLM, LlamaLens, for analyzing news and social media content in a multilingual context.<n>We demonstrate that LlamaLens outperforms the current state-of-the-art (SOTA) on 23 testing sets, and achieves comparable performance on 8 sets.
• Score: 9.539308087147134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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).

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