高效的训练库检索用于大型语言模型微调:癌症病例研究
Avisha Das1, Chiamaka Diala2, Guocai Chen2
1Mayo Clinic Arizona, Phoenix, AZ, USA.
Studies in health technology and informatics
|August 8, 2025
概括
我们开发了KnowledgePipeline,这是一款用于癌症研究的自动化工具,用于为大型语言模型 (LLM) 微调创建高质量的 corpora. 该工具增强了知识检索,并支持LLM在癌症研究中的应用,在特定领域实现了高相关性得分.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 在癌症研究当前的知识检索是有限的.
- 需要自动化工具来有效收集和分析数据.
研究的目的:
- 为癌症研究开发一个自动化知识提取工具 (KnowledgePipeline).
- 为微调大型语言模型 (LLM) 建立高质量的学术机构.
- 为了研究该工具在介质素-6和膀癌领域的有效性.
主要方法:
- 知识管道整合了学术论文内容,共同引用和共同作者网络.
- 两个LLM (GPTJ-6.7B和Galactica30B) 在特定领域的问答对上进行了微调.
- 评估重点是知识提取质量和微调模型在问答任务中的性能.
主要成果:
- 知识管道为特定领域的知识检索提供了一个可扩展的,自动化的框架.
- 获得了高相关性得分:IL-6为68%,膀癌为74.5%.
- 一个微调的Galactica-30B模型在问答方面展示了有希望的功能.
结论:
- 知识管道推进了文献发现,并解决了癌症研究中的关键生物医学挑战.
- 该工具为改善癌症研究结果提供了微调的LLM应用程序.
- 自动化知识提取对于提高专业科学领域的LLM能力至关重要.
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