解锁案例报告:利用大量语言模型来提升儿童心理虐待的研究
Dragan Stoll1, Andreas Jud2, Samuel Wehrli3
1Institute of Psychology and Education, Ulm University, Germany; School of Social Work, ZHAW Zurich University of Applied Sciences, Switzerland.
Child abuse & neglect
|August 30, 2025
概括
采用大型语言模型 (LLM) 的检索增强生成 (RAG) 准确地从叙述中提取儿童保护服务 (CPS) 的案例因素. 这种自动化方法与人类审查员的表现相匹配或超过, 帮助处于危险的儿童做出决策.
科学领域:
- 计算社会科学
- 在社会工作中使用人工智能
- 儿童福利的自然语言处理
背景情况:
- 由于案件档案中的非结构化数据,儿童保护服务 (CPS) 的研究面临挑战.
- 获取增强生成 (RAG) 和大型语言模型 (LLM) 显示出从叙述中提取结构化数据的潜力.
- 通过RAG自动分类可以减少手动注释数据的需要.
研究的目的:
- 使用RAG和LLM从叙述性CPS案例报告中提取结构化数据.
- 包括心理虐待,父母的合作和父母的酒精滥用.
- 评估RAG-LLM方法与复杂和简单案例因素的人类编码的性能.
主要方法:
- 一个四阶段的工作流程:收集病例报告,基于RAG的评估,自动提取和病例标签.
- 使用了29770份来自瑞士的CPS报告 (2008-2022年).
- 模型的性能与来自人类专家的共识数据集进行了验证.
主要成果:
- RAG-LLM的准确率很高:心理虐待为82%,缺乏家长合作为83%,家长滥用酒精为95%.
- 模型的表现超过了人类审查员的同意率 (分别为79%,80%,93%).
- 文本段中的上下文线索是有效的RAG提取的关键.
结论:
- 基于RAG的评估可以有效地复制人类对复杂CPS案例因素的判断.
- 对于父母滥用酒精等简单因素的准确性很高.
- RAG应用程序可以作为预警系统,支持专业人员识别关键因素并改善风险儿童的结果.
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