用大模型自动分析咨询对话，精准预测来访者对咨访关系的感知并生成可解释理由。
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models
- 基于大模型和专家标注理由，从咨询记录中多维度预测咨访关系质量。
- 与来访者自评相关性超70%提升，显著缩小咨询师评估与来访者感知差距。
- 生成有上下文依据的解释，适合心理咨询研究与临床辅助决策使用。
来访者对咨访关系的感知是心理辅导有效性的关键因素。传统会后问卷负担重且延迟明显，现有计算方法得分粗糙、缺乏可解释性理由，且无法建模完整会话背景。本文提出CARE框架，基于大语言模型自动预测多维度的咨访关系评分，并生成可解释理由。该框架基于CounselingWAI数据集，结合9,516条专家标注的解释理由，采用LLaMA-3.1-8B-Instruct作为基础模型，通过增强解释的监督方式进行微调。实验表明，CARE优于主流大模型，使咨询师评估与来访者感知之间的差距显著缩小，与来访者评分的皮尔逊相关系数提升超过70%。解释增强监督进一步提升了预测准确性。同时，CARE生成的解释经自动与人工评估均表现优良。在真实中文在线心理咨询会话中的应用揭示了常见的关系建立挑战，阐明互动模式如何影响关系发展，并提供可操作洞察，展示了其作为心理健康辅助工具的潜力。
原文摘要 · Abstract (English)
Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are burdensome and often delayed, while existing computational approaches produce coarse scores, lack interpretable rationales, and fail to model holistic session context. We present CARE, an LLM-based framework to automatically predict multi-dimensional alliance scores and generate interpretable rationales from counseling transcripts. Built on the CounselingWAI dataset and enriched with 9,516 expert-curated rationales, CARE is fine-tuned using rationale-augmented supervision with the LLaMA-3.1-8B-Instruct backbone. Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance, achieving over 70% higher Pearson correlation with client ratings. Rationale-augmented supervision further improves predictive accuracy. CARE also produces high-quality, contextually grounded rationales, validated by both automatic and human evaluations. Applied to real-world Chinese online counseling sessions, CARE uncovers common alliance-building challenges, illustrates how interaction patterns shape alliance development, and provides actionable insights, demonstrating its potential as an AI-assisted tool for supporting mental health care.
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