用个性化检索增强生成，让抑郁症检测更可解释。
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation
- 根据用户背景生成定制查询，提升临床文本检索精准度。
- 在真实数据集上准确率优于黑箱模型和普通LLM基线。
- 适合需要可解释性的精神健康AI系统研发者使用。
抑郁症是广泛存在的心理疾病，临床访谈是评估的金标准，但依赖稀缺专业人员。现有系统多采用不可解释的黑箱神经网络，部分尝试用事后大模型生成解释，却存在幻觉问题。为此，我们提出RED框架——一种用于可解释抑郁症检测的检索增强生成方法。该框架从临床访谈文本中检索证据，提供预测依据。传统基于查询的检索采用统一策略，难以适应个体差异。我们引入个性化查询生成模块，结合通用查询与由大模型推断的用户背景信息，实现上下文定制化检索。此外，为提升大模型在社会智能任务中的表现，我们通过事件为中心的检索器，从社会智能知识库中获取相关知识进行增强。在真实世界基准上的实验表明，RED在性能上显著优于神经网络及基于大模型的基线方法。
原文摘要 · Abstract (English)
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.
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