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基于检索增强生成和深度学习的大型语言模型的损伤程度评估

Fan Zhang¹, Yifang Luo², Zihuan Gao³

¹Ocean College, Zhejiang University, Zhoushan, 316000, Zhejiang, China; Kavli Institute for Astrophysics and Space Research Center, Massachusetts Institute of Technology, Cambridge 02139, MA, USA.

International journal of law and psychiatry

|February 19, 2025

概括

本研究增强了大型语言模型 (LLM) 用于法医伤害评估,使用检索增强生成 (RAG) 和知识图. 这种新的方法显著提高了LLM在中国应用中的准确性.

科学领域:

法医科学法医科学法医科学
人工智能的人工智能
自然语言处理自然语言处理.

背景情况:

大型语言模型 (LLM) 在一般NLP中表现出色,但由于域名知识有限,在法医伤害评估等专业领域扎.
准确检索相关的法律和医疗信息对于法医伤害评估至关重要.
现有的方法缺乏整合特定领域的知识库和LLM驱动评估的高级检索.

研究的目的:

开发一种新的方法,将Retrieval-Augmented Generation (RAG),基于图形的知识库和基于LLM的法医伤害评估的深度学习结合起来.
为了使LLM能够准确地应用中国身体伤害程度评估标准 (SAEBI).
提高LLMs在专业法医领域的准确性和适用性.

主要方法:

创建一个数据集,包括26,199个真实的伤害评估案例.
开发一个RoBERTa-CNN模型,准确地分类受伤位置和严重程度.
在RAG框架内将RoBERTa-CNN模型与基于图形的知识库集成,以提高LLM的性能.

主要成果:

拟议的RAG战略显著改善了九个最先进的LLM在伤害评估任务中的表现.
与传统的检索方法相比,准确度的提高在21至59个百分点之间.
额外的实验证明了该方法可以转移到其他领域,例如犯罪分类.

结论:

特定领域的知识,高级检索和深度学习的结合有效地提高了专业领域的LLM能力.
这种方法显示出使用人工智能提高法医伤害评估的准确性和效率的巨大潜力.
该方法在伤害评估和犯罪分类方面的成功凸显了其在专门的AI任务中的广泛应用.

关键词:

对损伤程度的评估深度学习是一种深度学习.大型语言模型.提取增强生成的提取.

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