Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
作者: Minbyul Jeong, Jiwoong Sohn, Mujeen Sung, Jaewoo Kang
分类: cs.CL, cs.AI, cs.IR
发布日期: 2024-01-27 (更新: 2024-06-18)
备注: ISMB 2024
💡 一句话要点
提出Self-BioRAG以解决生物医学推理问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 生物医学推理 检索增强生成 自我反思 医学问答 大型语言模型
📋 核心要点
- 现有的检索增强生成方法在处理特定领域问题时表现出较差的泛化能力，导致检索到错误文档或做出不准确判断。
- 本文提出的Self-BioRAG框架通过自我反思和领域特定文档检索，增强了生物医学文本生成的可靠性。
- 实验结果表明，Self-BioRAG在医学问答任务中相较于最先进的开源模型实现了7.2%的绝对性能提升。
📝 摘要（中文）
近年来，像GPT-4这样的专有大型语言模型（LLMs）在生物医学领域取得了重要进展，但仍面临一些无法通过编码知识解决的挑战。为此，本文提出了Self-BioRAG框架，专注于生成解释、检索领域特定文档和自我反思生成的响应。通过使用84k个过滤的生物医学指令集进行训练，Self-BioRAG能够评估其生成的解释，并在三个主要的医学问答基准数据集上实现了显著的性能提升，平均绝对提高7.2%。
🔬 方法详解
问题定义：本文旨在解决现有大型语言模型在生物医学领域推理时的局限性，特别是在检索相关文档和生成准确解释方面的不足。现有方法在特定领域问题上泛化能力差，常常导致错误的文档检索和不准确的判断。
核心思路：Self-BioRAG框架通过结合自我反思机制和领域特定文档检索，提升了生成文本的准确性和可靠性。该设计旨在使模型能够更好地理解问题并检索相关信息，从而生成更符合领域要求的回答。
技术框架：Self-BioRAG的整体架构包括三个主要模块：生成模块、检索模块和自我反思模块。生成模块负责生成初步回答，检索模块从知识库中检索相关文档，自我反思模块则评估生成的回答并进行必要的调整。
关键创新：最重要的技术创新在于引入了自我反思机制，使模型能够根据生成的回答进行自我评估，并通过定制的反思标记进行改进。这一机制与现有方法的根本区别在于其动态调整生成内容的能力。
关键设计：在模型训练中，使用了84k个过滤的生物医学指令集，确保模型能够适应特定领域的需求。此外，模型的参数设置和损失函数设计也经过精心调整，以优化生成质量和检索效果。
🖼️ 关键图片
📊 实验亮点
在三个主要医学问答基准数据集上的实验结果显示，Self-BioRAG相较于最先进的开源模型实现了平均7.2%的绝对性能提升，展现出显著的优势。这一提升表明，Self-BioRAG在处理生物医学文本生成任务时的有效性和可靠性。
🎯 应用场景
Self-BioRAG框架在生物医学和临床领域具有广泛的应用潜力，能够用于医学问答系统、临床决策支持以及医学教育等场景。通过提升模型的推理能力和生成质量，该研究有望改善医疗服务的效率和准确性，推动智能医疗的发展。
📄 摘要（原文）
Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice questions to long-form generations. To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation. However, when applying existing methods to different domain-specific problems, poor generalization becomes apparent, leading to fetching incorrect documents or making inaccurate judgments. In this paper, we introduce Self-BioRAG, a framework reliable for biomedical text that specializes in generating explanations, retrieving domain-specific documents, and self-reflecting generated responses. We utilize 84k filtered biomedical instruction sets to train Self-BioRAG that can assess its generated explanations with customized reflective tokens. Our work proves that domain-specific components, such as a retriever, domain-related document corpus, and instruction sets are necessary for adhering to domain-related instructions. Using three major medical question-answering benchmark datasets, experimental results of Self-BioRAG demonstrate significant performance gains by achieving a 7.2% absolute improvement on average over the state-of-the-art open-foundation model with a parameter size of 7B or less. Overall, we analyze that Self-BioRAG finds the clues in the question, retrieves relevant documents if needed, and understands how to answer with information from retrieved documents and encoded knowledge as a medical expert does. We release our data and code for training our framework components and model weights (7B and 13B) to enhance capabilities in biomedical and clinical domains.