基于 LLM 评估的心理治疗师-来访者互动建模实证研究

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Abstract:This study aimed to develop and validate a computational framework for modeling therapist--client interaction processes and to examine how therapist behaviors and relational qualities relate to client self-disclosure and emotional expression in psychotherapy sessions. We developed automated assessment pipelines using large language models (LLMs) to measure therapist behaviors (empathy, exploration), relational quality (rapport), and client outcomes (self-disclosure, self-directed negative emotion, outward-directed negative emotion). Model-generated scores were validated against human annotations. We then applied these measures to approximately 2,000 hours of psychotherapy transcripts from the Alexander Street corpus. This yielded an estimation sample of 119,233 client utterances from 1,573 sessions and 37 clients. Automated measures showed good to excellent agreement with human annotations (ICC = 0.60 - 0.81), with rapport (ICC = 0.81), self-disclosure (ICC = 0.78), empathy components (ICC = 0.60 - 0.75), and emotion measurements (ICC = 0.66 - 0.80). SEM analyses revealed that therapist empathy and exploration were each associated with increased client disclosure and increased expression of self-directed negative emotions. Both behaviors were also associated with reduced expressions of enjoyment. Surprisingly, rapport in the prior session showed no association with client self-disclosure or negative emotion expression. This study demonstrates that LLM-based measurement combined with structural modeling can capture core therapeutic processes at scale.

Submission history

From: Haiyi Zhu [view email]
[v1] Thu, 12 Feb 2026 22:14:07 UTC (806 KB)
[v2] Mon, 8 Jun 2026 00:59:55 UTC (730 KB)
[v3] Thu, 22 Oct 2026 15:08:56 UTC (342 KB)