案例报告解锁:利用大型语言模型推进儿童虐待研究
Dragan Stoll1, Samuel Wehrli2, David Lätsch1
1School of Social Work, ZHAW Zurich University of Applied Sciences, Switzerland.
Child abuse & neglect
|December 17, 2024
概括
大型语言模型 (LLM) 准确地从儿童保护服务 (CPS) 叙述中提取结构化数据,在分类暴力亚型方面表现优于人类编码器. 这有助于推进CPS研究,因为它可以从案件档案中进行复杂的数据分析.
科学领域:
- 计算语言学计算语言学
- 社会工作研究社会工作研究
- 儿童保护服务 信息学 信息学
背景情况:
- 儿童保护服务 (CPS) 的研究受到案件档案中缺乏结构化数据的阻碍.
- 自动化语言处理仅限于更简单的分类任务.
- 大型语言模型 (LLM) 提供了从叙述中提取更复杂数据的潜力.
研究的目的:
- 使用LLMs从叙事案例报告中提取结构化数据.
- 为了分类特定的暴力亚型:儿童性虐待,身体虐待,目击家庭暴力和儿童侵略.
主要方法:
- 开发了一个四个阶段的管道:文本细分,细分分类和报告/案例标签.
- 利用了来自瑞士最大的CPS提供商的29770份CPS报告 (2008-2022年).
- 通过使用注释数据,与人类审稿人进行LLM绩效比较.
主要成果:
- 最好的LLM (Mixtral-8x7B) 在文本分段分类中实现了87%的准确性,超过了人类协议 (77%).
- 在不提取的细分被排除在外时,LLM还在标记案例报告时实现了87%的准确性.
- 法学士的表现表明在案例数据中对复杂的上下文理解的能力.
结论:
- 法律学士可以准确地编码复杂的文本文件,大大推进儿童保护服务研究.
- 通过将决策追溯到特定的文本段来实现LLM标签的透明度.
- 确定基于关键字的细分作为一个限制,并强调需要解决潜在的偏见.
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