SocialSim：迈向情感支持对话的社交化模拟
SocialSim: Towards Socialized Simulation of Emotional Support Conversation
AI总结：
本文提出SocialSim框架，通过整合社交披露与社交意识模拟情感支持对话，构建高质量合成语料库SSConv并训练出性能领先的聊天机器人，使情感关怀更可及。
摘要：
情感支持对话（ESC）通过互动对话帮助减轻人们的心理压力并提供情感价值。由于众包构建大规模ESC语料库成本高昂，近期尝试使用大语言模型进行对话增强。然而，现有方法在很大程度上忽视了ESC中固有的社交动态，导致模拟效果不佳。在本文中，我们介绍了SocialSim，一种新颖的框架，通过整合社交互动的关键方面——社交披露和社交意识——来模拟ESC。在求助者方面，我们通过构建一个全面的角色库来促进社交披露，该角色库捕捉多样且真实的求助场景。在支持者方面，我们通过引发认知推理来增强社交意识，以生成逻辑且支持性的回应。基于SocialSim，我们构建了SSConv，一个大规模合成ESC语料库，其质量甚至可以超越众包的ESC数据。我们进一步在SSConv上训练了一个聊天机器人，并在自动评估和人工评估中展示了其最先进的性能。我们相信SocialSim提供了一种可扩展的ESC合成方式，使情感关怀更加可及和实用。
英文摘要：
Emotional support conversation (ESC) helps reduce people's psychological stress and provide emotional value through interactive dialogues. Due to the high cost of crowdsourcing a large ESC corpus, recent attempts use large language models for dialogue augmentation. However, existing approaches largely overlook the social dynamics inherent in ESC, leading to less effective simulations. In this paper, we introduce SocialSim, a novel framework that simulates ESC by integrating key aspects of social interactions: social disclosure and social awareness. On the seeker side, we facilitate social disclosure by constructing a comprehensive persona bank that captures diverse and authentic help-seeking scenarios. On the supporter side, we enhance social awareness by eliciting cognitive reasoning to generate logical and supportive responses. Building upon SocialSim, we construct SSConv, a large-scale synthetic ESC corpus of which quality can even surpass crowdsourced ESC data. We further train a chatbot on SSConv and demonstrate its state-of-the-art performance in both automatic and human evaluations. We believe SocialSim offers a scalable way to synthesize ESC, making emotional care more accessible and practical.
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。
