Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation

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 Retrievalaugmented 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-sizefits-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 eventcentric retriever.Experimental results on the real-world benchmark demonstrate RED's effectiveness compared to neural networks and LLM-based baselines.