用多模态检索增强生成技术，为哮喘患者提供多语言支持。
AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support
- 结合文本、图像、视频的多模态检索增强生成
- 在哮喘问答数据集上准确率显著优于通用模型
- 适合医疗资源匮乏地区患者使用
全球哮喘发病率上升，受环境与生活方式因素驱动。医疗资源获取受限，尤其在发展中国家，亟需自动化支持系统。尽管大型语言模型如ChatGPT和Gemini在自然语言处理与问答任务上取得进展，但易产生事实性错误（即幻觉）。通过整合精选文档的检索增强生成系统可提升大模型表现并减少幻觉。本文提出AsthmaBot，一个面向哮喘患者的多语言、多模态检索增强生成系统。在哮喘相关常见问题数据集上的评估表明其有效性。AsthmaBot配备交互式直观界面，融合文本、图像、视频等多种数据模态，提升公众可及性。系统已上线，可通过 <url>asthmabot.datanets.org</url> 访问。
原文摘要 · Abstract (English)
Asthma rates have risen globally, driven by environmental and lifestyle factors. Access to immediate medical care is limited, particularly in developing countries, necessitating automated support systems. Large Language Models like ChatGPT (Chat Generative Pre-trained Transformer) and Gemini have advanced natural language processing in general and question answering in particular, however, they are prone to producing factually incorrect responses (i.e. hallucinations). Retrieval-augmented generation systems, integrating curated documents, can improve large language models' performance and reduce the incidence of hallucination. We introduce AsthmaBot, a multi-lingual, multi-modal retrieval-augmented generation system for asthma support. Evaluation of an asthma-related frequently asked questions dataset shows AsthmaBot's efficacy. AsthmaBot has an added interactive and intuitive interface that integrates different data modalities (text, images, videos) to make it accessible to the larger public. AsthmaBot is available online via \url{asthmabot.datanets.org}.
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