通过纠正和丰富生成的数据库查询来引导真实的大型语言模型分析:迈向ChatGPT生物信息学的第一步
1Department of Developmental and Cell Biology, Center for Complex Biological Systems, University of California at Irvine, 4203 McGaugh Hall, Irvine, CA 92697, USA.
Briefings in bioinformatics
|February 6, 2025
概括
大型语言模型 (LLM) 在基因组学数据准确性方面扎. 我们推出了NagGPT,将LLM与数据库连接起来,改善生物信息学分析和事实正确性.
科学领域:
- 基因组学和生物信息学
- 科学研究中的人工智能
背景情况:
- 大型语言模型 (LLM) 具有事实知识,但往往缺乏完整性和检索准确性,特别是在基因组学等快速发展的科学领域.
- 现有的LLM,如ChatGPT,作为生物信息学助理具有局限性,包括数据检索不佳,幻觉和错误的序列操纵.
研究的目的:
- 通过开发一个以当前的权威数据为基础的系统来解决LLM在科学领域的局限性.
- 通过改善事实性和指令遵循来增强LLM对基因组学和生物信息学数据分析的能力.
主要方法:
- 介绍了NagGPT,它是一个中间件工具,旨在在LLM和数据库之间进行接口,管理查询和响应.
- 开发一个 OpenAI 自定义 GPT 的伴侣, Genomics Fetcher-Analyzer,它指示 ChatGPT 生成和执行用于生物信息学任务的 Python 代码,使用来自多个基因组学数据库的数据.
- 实施策略以缓解诸如代码生成问题,标识符混和数据幻觉等挑战.
主要成果:
- 展示NagGPT在弥合LLM知识差距和促进数据库API使用方面的有效性.
- 通过 ChatGPT 成功执行生物信息任务,由 Genomics Fetcher-Analyzer 指导,并以动态检索数据为动力.
- 与代码-数据交互,标识符模糊性和LLM幻觉有关的问题的部分缓解.
结论:
- 拟议的系统,纳入NagGPT和基因组学fetcher-analyzer,提供了一个可行的方法来增强LLMs作为专门的生物信息学助理.
- 研究结果表明,在科学背景下,可以提高未经修改的LLM的事实准确性和遵循指令的能力.
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