在儿童性虐待案件中,法医采访和审判证词的自动化问题类型编码
Zsofia A Szojka1, Suvimal Yashraj1, Thomas D Lyon1
1Gould School of Law, University of Southern California.
Law and human behavior
|March 20, 2025
概括
使用大型语言模型进行自动化问题类型编码,为分析法医采访和法庭证词提供了快速准确的手动方法替代方案. 这项技术改善了对虐待儿童案件的采访质量的评估.
科学领域:
- 计算语言学 计算语言学
- 法医心理学 法医心理学
- 儿童虐待研究 儿童虐待研究
背景情况:
- 问题类型的分类对于评估面试质量至关重要,但如果手动完成,则需要大量的劳动力.
- 需要自动化方法来有效评估涉及儿童虐待受害者的法医采访和法庭证词.
研究的目的:
- 评估大型语言模型的可靠性,用于在法医环境中进行自动化问题类型分类.
- 为了比较自动化模型与手动编码器的性能.
主要方法:
- 一个大型语言模型从法医采访和审判证词中训练了超过35万个问题.
- 该模型被测试了其将问题分类为类别的能力:邀请,wh-questions,选择性问题和非问题.
主要成果:
- 自动化模型实现了高可靠性 (95%的协议; κ = .93),与手动编码器相比.
- 手动编码器比模型犯的错误更多,特别是在识别邀请和非问题时.
- 纠正手动编码错误进一步提高了模型的可靠性 (98%的同意; κ = .97).
结论:
- 自动化问题类型编码是一种节省时间和准确的替代手动编码.
- 经过培训的模型是公开可用的,以帮助研究人员和从业人员评估法医采访质量.
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