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.

翻译：检测自杀意念、强奸、家庭暴力、儿童虐待及性骚扰等心理健康危机情境，是语言模型面临的一项关键却尚未被充分探索的挑战。当此类情境在用户与模型交互过程中出现时，模型必须可靠地予以标记，因为未能识别可能带来严重后果。本研究提出CRADLE BENCH，一个用于多维度危机检测的基准。与先前仅关注有限危机类型的研究不同，我们的基准依据临床标准定义了七种危机类型，并首次引入了时间维度标签。该基准提供600个临床专家标注的评估样本与420个开发样本，以及一个约4K样本的训练语料库，该语料库通过多语言模型多数投票集成实现自动标注，其性能显著优于单模型标注。我们进一步基于共识一致与全体一致集成协议定义的子集，微调了六个危机检测模型，从而提供了在不同一致性准则下训练的互补模型。