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相关实验视频

Updated: May 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用大型语言模型从患者衍生癌症模型的科学文本中提取知识:算法开发和验证.

Jiarui Yao1, Zinaida Perova2, Tushar Mandloi2

  • 1Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA 02115, USA.

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大型语言模型 (LLM) 可以自动从科学文本中提取患者衍生癌症模型 (PDCM) 信息. 软提示增强了较小的LLM在这个任务中与较大的专有模型相比较的性能.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 在瘤学中使用人工智能

背景情况:

  • 患者衍生癌症模型 (PDCMs) 对癌症研究和临床前研究至关重要,出版物显著增加.
  • 人工智能 (AI),特别是大型语言模型 (LLM),提供了从科学文献中提取大规模知识的潜力.

研究的目的:

  • 调查基于LLM的系统在科学文本中自动提取PDCM相关实体的有效性.
  • 用最先进的LLMs来比较直接提示和软提示方法.

主要方法:

  • 经过评估的直接提示 (手动提示带有说明,定义,示例) 和软提示 (自动训练的连续向量提示).
  • 使用专有GPT4-o和开放的LLaMA3家族模型进行实验.

主要成果:

  • 通过直接提示实现了GPT4-o的竞争性结果.
  • 软提示显著增强了较小的开放LLM,产生与专有模型可比的性能.
  • 证明了LLM在域特定文本提取方面的潜力.

结论:

  • 软提示是一种有效的技术,用于提高PDCM实体提取中较小的LLM的性能.
  • 针对特定任务和模型特征量身定制LLM方法对于科学文本挖掘的最佳结果至关重要.