生物医学知识图形优化的提示生成用于大型语言模型
Karthik Soman1, Peter W Rose2, John H Morris3
1Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, United States.
Bioinformatics (Oxford, England)
|September 17, 2024
概括
本研究介绍了一个基于知识图的提取增强生成 (KG-RAG) 框架. 在生物医学任务中,KG-RAG显著提高了大型语言模型的性能,减少了代币消耗和提高了准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 在生物医学等知识密集型领域面临挑战.
- 目前的解决方案,如预培训和微调,需要高的计算成本,需要领域专业知识.
研究的目的:
- 引入一个以代币优化的基于知识图的检索增强生成 (KG-RAG) 框架.
- 利用SPOKE生物医学知识图 (KG) 与LLMs来生成准确的生物医学文本.
主要方法:
- 开发了一个KG-RAG框架,使用SPOKE KG和LLMs (Llama-2,GPT-3.5,GPT-4).
- 通过嵌入方法实现了使用最小图形图表和上下文修剪的上下文提取.
- 在专有LLM上优化代币消耗,以实现成本效益高的RAG.
主要成果:
- 在不影响准确性的情况下,在代币消费方面实现了超过50%的减少.
- 增强跨多种生物医学提示的LLM性能,提供来源和统计证据.
- 在一个具有挑战性的多选题数据集上,Llama-2模型的性能提高了71%.
结论:
- 该KG-RAG框架有效地结合了明确的KG知识和隐含的LLM知识以一种代币优化的方式.
- 这种方法提高了用于特定领域的生物医学问题的通用LLM的适应性,并且具有成本效益.
- 该框架为专业任务提供了开源模型,并提高了专有LLM的性能.
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