Improving the accessibility of psychotherapy with the aid of Large Language Models (LLMs) is garnering a significant attention in recent years. Recognizing cognitive distortions from the interviewee's utterances can be an essential part of psychotherapy, especially for cognitive behavioral therapy. In this paper, we propose ERD, which improves LLM-based cognitive distortion classification performance with the aid of additional modules of (1) extracting the parts related to cognitive distortion, and (2) debating the reasoning steps by multiple agents. Our experimental results on a public dataset show that ERD improves the multi-class F1 score as well as binary specificity score. Regarding the latter score, it turns out that our method is effective in debiasing the baseline method which has high false positive rate, especially when the summary of multi-agent debate is provided to LLMs.

翻译：近年来，借助大语言模型提升心理治疗的可及性引起了广泛关注。从受访者话语中识别认知扭曲是心理治疗（尤其是认知行为疗法）的关键环节。本文提出ERD框架，通过（1）提取与认知扭曲相关的文本片段，以及（2）多智能体辩论推理步骤等辅助模块，提升基于大语言模型的认知扭曲分类性能。在公开数据集上的实验结果表明，ERD框架在提升多分类F1得分的同时，也改善了二值特异性指标。关于后者，我们发现将多智能体辩论摘要提供给大语言模型时，该方法能有效消除基线方法中较高的假阳性率偏差。