通过矢量嵌入增强大型语言模型,以提高特定域的响应性
Nathan M Wolfrath1, Nathaniel B Verhagen2, Bradley H Crotty3
1Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin; Inception Health Labs, Medical College of Wisconsin.
本研究提出了一种方法,使用嵌入式将大型语言模型 (LLM) 更新为当前的科学数据. 这种方法提高了专业领域的LLM准确性,而无需重新训练整个模型.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 生物信息学是一种生物信息学.
背景情况:
- 大型语言模型 (LLM) 在静态数据上进行训练,限制它们在动态领域的使用.
- 当前的LLM与快速变化的信息,专有数据和敏感数据集作斗争.
- 针对特定领域的LLM适应对于科学研究等领域至关重要.
研究的目的:
- 概述用特定领域的最新科学信息来增强通用LLM (基础模型) 的方法.
- 为了使目前的,同行评审的科学手稿用于LLM增强.
- 促进专门领域的LLM系统的发展.
主要方法:
- 基于嵌入的方法用于将新的文本数据纳入现有LLM中.
- 使用的是像Llama-Index这样的开源工具和像Llama-2这样的公开模型.
- 讨论了增强后评估模型性能的方法.
主要成果:
- 描述的方法允许将当前的科学文献整合到LLMs中.
- 这种方法增强了LLM的领域特定知识,而不需要完全的再培训.
- 为增强的LLM系统提供了绩效评估方法.
结论:
- 这种方法可以快速开发专门的LLM系统.
- 它克服了动态知识领域LLM的静态培训机构的局限性.
- 该方法促进了LLM开发中的透明度,用户隐私和可复制性.
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