LlamaLens专精多语言新闻与社交媒体分析，性能超越现有模型。
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
- 基于18项任务52个数据集，针对性微调多语言模型
- 在23个测试集上超越当前最佳模型，8个持平
- 首个兼顾领域特异性和多语言的新闻社交分析模型
大型语言模型在通用任务中表现优异，但在特定领域如新闻和社交媒体内容分析方面仍受限。研究表明，针对下游NLP任务指令数据微调的模型优于未微调版本。现有研究多集中于英语等资源丰富语言及宽泛领域，对多语言与特定领域的结合关注不足。为此，本文提出LlamaLens，首个面向新闻与社交媒体内容分析的多语言专用LLM。实验涵盖阿拉伯语、英语、印地语共18项任务、52个数据集。结果显示，LlamaLens在23个测试集上超越当前最先进水平，8个测试集表现相当。模型与资源已公开（https://huggingface.co/collections/QCRI/llamalens-672f7e0604a0498c6a2f0fe9）。
原文摘要 · Abstract (English)
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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