用流式处理+证据记忆，让大模型生成可追溯的心理档案
From Speech to Profile: A Protocol-Driven LLM Agent for Psychological Profile Generation
- 分步处理咨询语音，用层级证据库存证
- 在真实青少年咨询数据上零幻觉，准确率高
- 适合临床心理、医疗AI领域研究人员
心理档案对抑郁症患者的治疗至关重要。大语言模型可用于从心理咨询对话中生成档案，但因语音过长、多方互动和无结构聊天，易出现长时遗忘和不可验证的幻觉。为此，我们提出 StreamProfile 流式框架：通过增量处理咨询语音，将自动语音识别（ASR）转录内容存储于分层证据记忆（Hierarchical Evidence Memory）中，并基于PM+心理干预方法执行思维链推理，最终仅使用原始证据合成档案，确保每条结论可追溯。在真实青少年咨询数据上的实验表明，该系统能准确生成心理档案且有效防止幻觉。
原文摘要 · Abstract (English)
The psychological profile that structurally documents the case of a depression patient is essential for psychotherapy. Large language models can be applied to summarize the profiles from counseling speech, however, it may suffer from long-context forgetting and produce unverifiable hallucinations, due to overlong length of speech, multi-party interactions and unstructured chatting. Hereby, we propose a StreamProfile, a streaming framework that processes counseling speech incrementally, extracts evidences grounded from ASR transcriptions by storing it in a Hierarchical Evidence Memory, and then performs a Chain-of-Thought pipeline according to PM+ psychological intervention for clinical reasoning. The final profile is synthesized strictly from those evidences, making every claim traceable. Experiments on real-world teenager counseling speech have shown that the proposed StreamProfile system can accurately generate the profiles and prevent hallucination.
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