基于个性化检索增强生成的临床访谈可解释抑郁症检测
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation
AI总结：
针对临床访谈中抑郁症自动化检测缺乏可解释性及LLM幻觉问题，提出检索增强生成框架RED，通过个性化查询生成与社交智能知识检索提供可解释的预测证据，实验证明其优于现有基线。
摘要：
抑郁症是一种广泛存在的心理健康障碍，临床访谈是评估的黄金标准。然而，其对稀缺专业人员的依赖凸显了自动化检测的需求。现有系统主要采用黑盒神经网络，缺乏可解释性，而这在心理健康情境中至关重要。一些提升可解释性的尝试使用事后LLM生成，但存在幻觉问题。为解决这些局限，我们提出了RED，一个用于可解释抑郁症检测的检索增强生成框架。RED从临床访谈记录中检索证据，为预测提供解释。传统基于查询的检索系统采用一刀切的方法，这可能不适用于抑郁症检测，因为用户背景和情况各异。我们引入了个性化查询生成模块，将标准查询与LLM推断的用户特定背景相结合，根据个体上下文定制检索。此外，为增强LLM在社交智能方面的表现，我们通过使用以事件为中心的检索器从社交智能知识库中检索相关知识来增强LLM。在真实世界基准上的实验结果证明了RED相比神经网络和基于LLM的基线的有效性。
英文摘要：
Depression is a widespread mental health disorder, and clinical interviews are the gold standard for assessment. However, their reliance on scarce professionals highlights the need for automated detection. Current systems mainly employ black-box neural networks, which lack interpretability, which is crucial in mental health contexts. Some attempts to improve interpretability use post-hoc LLM generation but suffer from hallucination. To address these limitations, we propose RED, a Retrieval-augmented generation framework for Explainable depression Detection. RED retrieves evidence from clinical interview transcripts, providing explanations for predictions. Traditional query-based retrieval systems use a one-size-fits-all approach, which may not be optimal for depression detection, as user backgrounds and situations vary. We introduce a personalized query generation module that combines standard queries with user-specific background inferred by LLMs, tailoring retrieval to individual contexts. Additionally, to enhance LLM performance in social intelligence, we augment LLMs by retrieving relevant knowledge from a social intelligence datastore using an event-centric retriever. Experimental results on the real-world benchmark demonstrate RED's effectiveness compared to neural networks and LLM-based baselines.
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。
