LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
It addresses the gap in domain-specific and multilingual NLP for news and social media analysis, though it is incremental as it builds on fine-tuning existing models.
The study developed LlamaLens, a specialized multilingual LLM for analyzing news and social media content, which outperformed SOTA on 23 out of 52 datasets across Arabic, English, and Hindi tasks.
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).