用大模型推理能力提升心理认知重构效果，验证了思维链等方法的有效性。
Does "Reasoning" with Large Language Models Improve Recognizing, Generating, and Reframing Unhelpful Thoughts?
- 引入思维链和自一致性等推理策略增强大模型的心理认知重构能力。
- 即使在GPT-3.5上，增强推理方法也优于当前最先进的预训练推理模型。
- 适用于心理健康辅助系统研发，尤其关注认知重构的实践应用。
认知重构是认知行为疗法（CBT）的核心，帮助个体通过发现积极意义重新解读负面经历。近期大型语言模型（LLMs）通过基于推理的策略展现出性能提升。这启发我们利用LLM的推理能力模拟批判性思维过程，以改进CBT中的认知重构，从而更有效地识别、生成和重构非建设性想法。本文研究了多种推理方法的作用，包括预训练推理型LLM以及思维链（CoT）和自一致性等增强推理策略，以提升模型在认知重构任务上的表现。结果表明，即便应用于较旧的模型如GPT-3.5，增强推理方法在识别、生成和重构非建设性想法方面仍持续优于当前最先进的预训练推理模型。
原文摘要 · Abstract (English)
Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large Language Models (LLMs) have demonstrated improved performance through reasoning-based strategies. This inspires a promising direction of leveraging the reasoning capabilities of LLMs to improve CBT and mental reframing by simulating the process of critical thinking, potentially enabling more effective recognition, generation, and reframing of cognitive distortions. In this work, we investigate the role of various reasoning methods, including pre-trained reasoning LLMs and augmented reasoning strategies such as CoT and self-consistency in enhancing LLMs' ability to perform cognitive reframing tasks. We find that augmented reasoning methods, even when applied to "outdated" LLMs like GPT-3.5, consistently outperform state-of-the-art pretrained reasoning models on recognizing, generating and reframing unhelpful thoughts.
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