通过强化推理提升对话支持的逻辑与共情能力
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation
- 基于原始数据引导模型生成有逻辑的支持回应
- 使用强化学习优化推理过程，提升回应质量
- 适合研究情感对话与认知推理融合的学者
情感支持对话（ESC）在缓解心理压力、提供情感价值方面具有重要作用。尽管近期研究多聚焦于数据增强与合成语料构建，却常忽视有效情感支持背后的深层认知推理过程。为此，我们提出CARE框架，无需依赖大规模合成数据即可增强ESC中的推理能力。CARE利用原始ESC训练集指导模型生成逻辑连贯且具支持性的回复，从而显式提升认知推理能力。在此基础上，进一步采用强化学习对推理过程进行精炼与强化。实验结果表明，CARE显著提升了回复的逻辑严谨性与支持性，推动了更具同理心、认知稳健且类人的情感支持系统发展。
原文摘要 · Abstract (English)
Emotional Support Conversation (ESC) plays a vital role in alleviating psychological stress and providing emotional value through dialogue. While recent studies have largely focused on data augmentation and synthetic corpus construction, they often overlook the deeper cognitive reasoning processes that underpin effective emotional support. To address this gap, we propose \textbf{CARE}, a novel framework that strengthens reasoning in ESC without relying on large-scale synthetic data. CARE leverages the original ESC training set to guide models in generating logically coherent and supportive responses, thereby explicitly enhancing cognitive reasoning. Building on this foundation, we further employ reinforcement learning to refine and reinforce the reasoning process. Experimental results demonstrate that CARE significantly improves both the logical soundness and supportive quality of responses, advancing the development of empathetic, cognitively robust, and human-like emotional support systems.
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