癌症患者问答系统中的查询管道优化
Query pipeline optimization for cancer patient question answering systems
AI总结：
本文提出了一种针对癌症患者问答系统的RAG查询管道三方面优化方法，通过改进文档检索、段落检索和语义表示，提升了回答准确性。
摘要：
检索增强生成（RAG）通过使用查询管道检索相关外部信息来减轻大型语言模型（LLMs）的幻觉问题。然而，癌症患者问答（CPQA）系统中的查询管道优化需要分别优化多个组件并考虑领域特定因素。我们提出了一种新的三方面优化方法，利用公共生物医学数据库如PubMed和PubMed Central。我们的优化包括：（1）文档检索，利用NCBI资源的比较分析并引入混合语义实时文档检索（HSRDR）；（2）段落检索，确定密集检索器和重排序器的最佳配对；（3）语义表示，引入语义增强重叠分割（SEOS）以提高上下文理解。在定制开发的癌症相关查询数据集上，我们的优化RAG方法在Claude-3-haiku上的回答准确性比链式推理提示提高了5.24%，比朴素RAG设置提高了约3%。本研究强调了领域特定查询优化在实现RAG全部潜力中的重要性，并提供了一个稳健的框架，用于构建更准确可靠的CPQA系统，推动RAG基于生物医学系统的开发。
英文摘要：
Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain-specific considerations. We propose a novel three-aspect optimization approach for the RAG query pipeline in CPQA systems, utilizing public biomedical databases like PubMed and PubMed Central. Our optimization includes: (1) document retrieval, utilizing a comparative analysis of NCBI resources and introducing Hybrid Semantic Real-time Document Retrieval (HSRDR); (2) passage retrieval, identifying optimal pairings of dense retrievers and rerankers; and (3) semantic representation, introducing Semantic Enhanced Overlap Segmentation (SEOS) for improved contextual understanding. On a custom-developed dataset tailored for cancer-related inquiries, our optimized RAG approach improved the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. This study highlights the importance of domain-specific query optimization in realizing the full potential of RAG and provides a robust framework for building more accurate and reliable CPQA systems, advancing the development of RAG-based biomedical systems.
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