用13类细粒度行为识别心理咨询中的抗拒，提升大模型理解力。
RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models
- 基于心理学理论构建13类抗拒行为识别框架
- 在2.4万条真实对话中实现91.25%抗拒识别率
- 适合心理辅导系统开发与临床干预研究
在文本式心理辅导中，识别并应对来访者抗拒行为至关重要，但现有NLP方法对抗拒类别简化处理，忽视治疗过程的时序动态，且解释性不足。为此，我们提出PsyFIRE框架，捕捉13种细粒度抗拒行为及协作互动。基于此，我们构建了包含23,930条标注语句的ClientResistance语料库，每条均附上下文相关的解释依据。利用该数据集，我们开发了RECAP——一个两阶段检测框架，可识别抗拒行为并提供细粒度分类与解释。RECAP在区分协作与抗拒上达到91.25% F1，细粒度分类宏平均F1达66.58%，超越领先提示式大模型基线超20个百分点。在独立辅导数据集及62名咨询师的试点研究中，RECAP揭示了抗拒的普遍性及其对治疗关系的负面影响，展现了提升咨询师理解与干预策略的潜力。
原文摘要 · Abstract (English)
Recognizing and navigating client resistance is critical for effective mental health counseling, yet detecting such behaviors is particularly challenging in text-based interactions. Existing NLP approaches oversimplify resistance categories, ignore the sequential dynamics of therapeutic interventions, and offer limited interpretability. To address these limitations, we propose PsyFIRE, a theoretically grounded framework capturing 13 fine-grained resistance behaviors alongside collaborative interactions. Based on PsyFIRE, we construct the ClientResistance corpus with 23,930 annotated utterances from real-world Chinese text-based counseling, each supported by context-specific rationales. Leveraging this dataset, we develop RECAP, a two-stage framework that detects resistance and fine-grained resistance types with explanations. RECAP achieves 91.25% F1 for distinguishing collaboration and resistance and 66.58% macro-F1 for fine-grained resistance categories classification, outperforming leading prompt-based LLM baselines by over 20 points. Applied to a separate counseling dataset and a pilot study with 62 counselors, RECAP reveals the prevalence of resistance, its negative impact on therapeutic relationships and demonstrates its potential to improve counselors' understanding and intervention strategies.
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