Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world applications. Large Language Models (LLMs) have shown strong empathetic abilities in ESC tasks. Yet, existing methods overlook the issue of cognitive distortions in help-seekers' expressions. As a result, current models can only provide basic emotional comfort, rather than helping help-seekers address their psychological distress at a deeper cognitive level. To address this challenge, we construct the CogBiasESC dataset, the first dataset that expands existing ESC datasets by adding labels for cognitive distortions, includes their type, intensity, and safe risk level. Furthermore, we propose the Cognitive Policy-driven Large Language Model framework (CoPoLLM) to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. We also analyze the safety advantages of CoPoLLM from a theoretical perspective. Experimental results show that CoPoLLM significantly outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control.

翻译：情感支持对话（ESC）在心理健康援助中扮演关键角色，通过在实际应用中提供可及的心理支持。大语言模型（LLMs）在ESC任务中展现出强大的共情能力。然而，现有方法忽视了求助者表达中的认知扭曲问题。因此，当前模型仅能提供基础情感安慰，而无法在更深认知层面上帮助求助者解决心理困扰。为应对这一挑战，我们构建了CogBiasESC数据集——首个通过添加认知扭曲标注（包括类型、强度和安全风险等级）来扩展现有ESC数据集的数据集。进一步，我们提出认知策略驱动大语言模型框架（CoPoLLM），以增强大语言模型诊断和干预求助者认知扭曲的能力。我们亦从理论角度分析了CoPoLLM的安全性优势。实验结果表明，CoPoLLM在扭曲诊断准确性、干预策略有效性及安全风险控制方面显著优于15个当前最优基线模型。