构建首个含时间标签的多维度心理危机检测基准
CRADLE Bench: A Clinician-Annotated Benchmark for Multi-Faceted Mental Health Crisis and Safety Risk Detection
- 基于临床标准设计七类危机场景，引入时间标注机制
- 包含600条医生标注测试样本，420条开发样本，4000条自动标注训练数据
- 提供不同共识标准下的模型，适合安全检测与医疗对话系统研究
检测自杀意念、性侵、家庭暴力、儿童虐待及性骚扰等心理健康危机，是语言模型面临的关键挑战。当用户与模型互动中出现此类情况时，模型需可靠识别，否则可能造成严重后果。本文提出CRADLE BENCH，一个面向多维度危机检测的基准。不同于以往仅覆盖少数危机类型的工作，本基准涵盖七类符合临床标准的危机类型，并首次引入时间标签。基准包含600条临床医生标注的评估样本和420条开发样本，另配有约4000条通过多模型多数投票集成自动标注的训练数据，其标注质量显著优于单模型标注。我们还基于共识与一致性的集成判断标准，对六种危机检测模型进行微调，提供不同标注标准下的互补模型。
原文摘要 · Abstract (English)
Detecting mental health crisis situations such as suicide ideation, rape, domestic violence, child abuse, and sexual harassment is a critical yet underexplored challenge for language models. When such situations arise during user--model interactions, models must reliably flag them, as failure to do so can have serious consequences. In this work, we introduce CRADLE BENCH, a benchmark for multi-faceted crisis detection. Unlike previous efforts that focus on a limited set of crisis types, our benchmark covers seven types defined in line with clinical standards and is the first to incorporate temporal labels. Our benchmark provides 600 clinician-annotated evaluation examples and 420 development examples, together with a training corpus of around 4K examples automatically labeled using a majority-vote ensemble of multiple language models, which significantly outperforms single-model annotation. We further fine-tune six crisis detection models on subsets defined by consensus and unanimous ensemble agreement, providing complementary models trained under different agreement criteria.
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