通过检索和自我反思来改善医疗推理,使用检索增强的大型语言模型来进行自我反思
Minbyul Jeong1, Jiwoong Sohn1, Mujeen Sung2
1Department of Computer Science, Korea University, Seoul 02841, Republic of Korea.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
自我BioRAG通过整合特定领域的检索和自我反思来增强生物医学任务的大型语言模型 (LLM). 这一框架提高了医疗问答和文本生成的准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
背景情况:
- 大型语言模型 (LLM) 在生物医学任务中表现有前途,但在特定领域的知识方面存在困难.
- 检索增强生成 (RAG) 方法通过整合外部文档来改进LLM,但在专业领域经常面临泛化问题.
研究的目的:
- 介绍Self-BioRAG,这是一个新的框架,旨在提供可靠的生物医学文本生成,解释和自我反思.
- 解决现有的RAG方法在特定领域的应用中的局限性,特别是在医疗领域.
主要方法:
- 开发了Self-BioRAG,一个框架,包括一个特定领域的检索器,一个专门的文档库和定制的指令集.
- 使用84,000个过的生物医学指令集训练模型,使其能够使用反射令牌自我评估生成的解释.
- 为了评估,利用了三个主要的医学问答基准数据集.
主要成果:
- 自我BioRAG在医疗问题答案任务上比最先进的开放基础模型 (7B或更少的参数) 实现了7.2%的平均绝对性能改善.
- 超过了标准RAG的平均8% Rouge-1分数在长形式的问答基准.
- 证明了对特定领域的组件的必要性,以有效遵守生物医学指令.
结论:
- 自我BioRAG通过检索相关信息并将其与编码知识合成,有效地模仿一名医学专家.
- 该框架显示了生物医学文本生成和问答能力的重大进步.
- 发布数据,代码和模型权重 (7B和13B),以促进生物医学和临床领域的进一步研究和开发.
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