ERD：一个用于改进 LLM 推理以进行认知扭曲分类的框架
ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification
AI总结：
本文提出 ERD 框架，通过提取认知扭曲相关部分和多智能体辩论推理步骤来提升 LLM 的认知扭曲分类性能，有效提高了多类 F1 和二分类特异性分数，并成功对高假阳性率的基线方法进行了去偏。
摘要：
近年来，借助大型语言模型（LLMs）提高心理治疗的可及性正受到广泛关注。从受访者的言语中识别认知扭曲可能是心理治疗的重要组成部分，尤其是对于认知行为疗法。在本文中，我们提出了 ERD，它通过附加模块的辅助来提升基于 LLM 的认知扭曲分类性能，这些模块包括：(1) 提取与认知扭曲相关的部分，以及 (2) 由多个智能体对推理步骤进行辩论。我们在公开数据集上的实验结果表明，ERD 提高了多类 F1 分数以及二分类特异性分数。关于后一项分数，结果表明我们的方法在对基线方法进行去偏方面是有效的，该基线方法具有较高的假阳性率，特别是当多智能体辩论的摘要提供给 LLMs 时。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
