超越单轮交互：将拒绝重新定义为嵌入心理健康支持互动情境的动态体验
Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded in the Context of Mental Health Support Interactions with LLMs
AI总结：
通过混合方法研究，揭示LLM在心理健康支持中的拒绝行为是多阶段动态体验，并提出多阶段评估框架和设计建议。
摘要：
内容警告：本文包含与心理健康挑战、情绪困扰和自杀意念相关的参与者引述和讨论。大型语言模型（LLM）越来越多地用于心理健康支持，但模型的安全措施——特别是拒绝参与敏感内容的行为——从用户和心理健康专业人员（MHP）的角度来看仍然缺乏理解，并且已被报道造成现实世界的伤害。本文展示了一项顺序混合方法研究的结果，该研究考察了在心理健康支持互动中LLM拒绝行为如何被体验和解释。通过对使用LLM进行心理健康支持的个人和MHP进行问卷调查（N=53）和深度访谈（N=16），我们发现拒绝行为不是孤立的单轮系统行为，而是构成动态的多阶段体验：拒绝前期望形成、拒绝触发与遭遇、拒绝消息框架、资源转介提供以及拒绝后结果。我们贡献了一个超越二元政策合规准确性的多阶段评估框架，以及未来拒绝机制的设计建议。这些发现表明，理解LLM拒绝行为需要超越单轮交互，将其视为嵌入用户支持寻求轨迹和更广泛的LLM设计管道中的整体体验。
英文摘要：
Content Warning: This paper contains participant quotes and discussions related to mental health challenges, emotional distress, and suicidal ideation. Large language models (LLMs) are increasingly used for mental health support, yet the model safeguards -- particularly refusals to engage with sensitive content -- remain poorly understood from the perspectives of users and mental health professionals (MHPs) and have been reported to cause real-world harms. This paper presents findings from a sequential mixed-methods study examining how LLM refusals are experienced and interpreted in mental health support interactions. Through surveys (N=53) and in-depth interviews (N=16) with individuals using LLMs for mental health support and MHPs, we reveal that refusals are not isolated, single-turn system behaviors but rather constitute dynamic, multi-phase experiences: pre-refusal expectation formation, refusal triggering and encounter, refusal message framing, resource referral provision, and post-refusal outcomes. We contribute a multi-phase framework for evaluating refusals beyond binary policy compliance accuracy and design recommendations for future refusal mechanisms. These findings suggest that understanding LLM refusals requires moving beyond single-turn interactions toward recognizing them as holistic experiences embedded within users' support-seeking trajectories and the broader LLM design pipeline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
