CARE: 基于认知推理的增强型情感支持对话
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support Conversation
AI总结：
CARE通过增强认知推理提升情感支持对话的质量，无需依赖大规模合成数据，实验显示其在逻辑严谨性和支持性方面有显著改进。
摘要：
情感支持对话（ESC）在缓解心理压力和通过对话提供情感价值方面起着至关重要的作用。尽管最近的研究大多集中在数据增强和合成语料库的构建上，但它们往往忽略了支撑有效情感支持的深层认知推理过程。为了解决这一缺口，我们提出了CARE，一种新的框架，通过不依赖大规模合成数据来加强ESC中的推理。CARE利用原始ESC训练集来指导模型生成逻辑一致且支持性的响应，从而显式地增强认知推理。在此基础上，我们进一步采用强化学习来优化和强化推理过程。实验结果表明，CARE显著提高了响应的逻辑严谨性和支持质量，推动了具有同理心、认知稳健性和人类样情感支持系统的发展。
英文摘要：
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.
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。
