从检索增强生成到智能体化：利用LLM智能体验证伊斯兰医学回答
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents
AI总结：
本研究提出Tibbe-AG评估流程，结合检索增强与智能体评判验证伊斯兰医学回答，使事实准确性提升23%，实现文化敏感的可靠医学问答。
摘要：
阿维森纳的《医典》和先知医学《提布-纳巴维》等数百年历史的伊斯兰医学文献蕴含丰富的预防保健、营养和整体疗法知识，但至今仍难以被大众获取，并在现代AI系统中未得到充分利用。现有的语言模型基准主要聚焦于事实回忆或用户偏好，在规模化验证文化背景下的医学指导方面存在空白。我们提出了一个统一的评估流程Tibbe-AG，将30个精心策划的先知医学问题与人工验证的疗法对齐，并在三种配置下比较三个LLM（LLaMA-3、Mistral-7B、Qwen2-7B）：直接生成、检索增强生成和科学自我批评过滤。每个回答随后由作为智能体评判者的次级LLM评估，生成单一的3C3H质量评分。检索将事实准确性提高了13%，而智能体提示通过更深入的机制洞察和安全考虑又增加了10%的提升。我们的结果表明，将古典伊斯兰文献与检索和自我评估相结合，能够实现可靠且文化敏感的医学问答。
英文摘要：
Centuries-old Islamic medical texts like Avicenna's Canon of Medicine and the Prophetic Tibb-e-Nabawi encode a wealth of preventive care, nutrition, and holistic therapies, yet remain inaccessible to many and underutilized in modern AI systems. Existing language-model benchmarks focus narrowly on factual recall or user preference, leaving a gap in validating culturally grounded medical guidance at scale. We propose a unified evaluation pipeline, Tibbe-AG, that aligns 30 carefully curated Prophetic-medicine questions with human-verified remedies and compares three LLMs (LLaMA-3, Mistral-7B, Qwen2-7B) under three configurations: direct generation, retrieval-augmented generation, and a scientific self-critique filter. Each answer is then assessed by a secondary LLM serving as an agentic judge, yielding a single 3C3H quality score. Retrieval improves factual accuracy by 13%, while the agentic prompt adds another 10% improvement through deeper mechanistic insight and safety considerations. Our results demonstrate that blending classical Islamic texts with retrieval and self-evaluation enables reliable, culturally sensitive medical question-answering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
