基于LLM的关键词增强用于标题驱动的证据选择:一种实际的方法
1Department of Forensic Sciences, Sungkyunkwan University, Seoul, South Korea.
Journal of forensic sciences
|March 13, 2026
概括
本研究介绍了用于数字法医调查的大型语言模型 (LLM) 关键字增强方法. 该方法增强了证据的发现,并减少了基于关键字的搜索中的经验差距.
科学领域:
- 数字法医学数字法医学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 数字取证中的基于关键字的搜索依赖于经验,导致不一致的结果.
- 人工智能解决方案的实际部署受到数据机密性和基础设施成本的阻碍.
研究的目的:
- 开发一种基于LLM的实用关键词增强方法,用于数字法医证据分类.
- 为了提高关键字搜索的有效性,而不影响敏感的案例数据.
主要方法:
- 使用语义相似性和关键字覆盖范围将文件名与文档体联系起来.
- 在基准数据集上评估来自ChatGPT模型的即时单独关键字生成.
- 与数字法医调查人员一起进行可用性研究,以评估增强关键字的影响.
主要成果:
- 文件名与文档内容有显著的相关性,与随机配对不同.
- 聊天GPT模型,特别是ChatGPT-4.1,在关键字生成方面表现出有效的检索性能.
- 增强关键词显著改善了证据的检测,特别是对于初级调查人员.
结论:
- 基于LLM的关键词增强方法可以使用文件名进行高效的证据分类.
- 这种方法通过减少与经验相关的绩效差异来支持大规模和分布式调查.
- 该方法为增强数字法医调查的实用解决方案,同时保持数据隐私.
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