通过情感偏好优化与Mamba压缩器，提升对话机器人的情感理解与长时记忆能力。
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
- 用情感偏好优化训练模型区分正反情绪响应，增强细微情感辨识力。
- MambaCompressor使长对话历史压缩效率提升，内存和时间复杂度显著降低。
- 适合需要高情感智能的客服与心理健康应用，尤其在长对话场景下表现优异。
聊天机器人研究正日益重要，尤其在需要人际互动的领域如客户服务与心理健康支持中。尽管取得进展，聊天机器人仍难以捕捉细微情绪变化并管理长对话历史。为此，本研究提出双策略：首先，采用情感偏好优化（EPO）训练模型，不仅学习正确回应，还学习情境相似但情绪相反的反向回应，从而提升对情绪细微差别的识别能力；其次，引入MambaCompressor，高效压缩并管理长对话历史，在显著降低时间和内存开销的同时增强上下文理解。多数据集上的综合实验表明，该模型在生成共情回应及处理长对话方面均显著优于现有方法。
原文摘要 · Abstract (English)
Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these advancements, chatbots still face significant challenges in understanding subtle nuances and managing long conversation histories. To address these issues, our study introduces a dual approach: firstly, we employ Emotional Preference Optimization (EPO) to train chatbots not only with correct responses but also with counter-emotional responses-those that are contextually similar but emotionally divergent. This training enables the model to discern fine nuance distinctions between correct and counter-emotional responses, thereby enhancing the quality of its responses. Secondly, we introduce MambaCompressor to effectively compress and manage extensive conversation histories, significantly reducing time and memory complexities while improving the chatbot's contextual understanding. Our comprehensive experiments across multiple datasets demonstrate that our model significantly outperforms existing models in generating empathetic responses and efficiently managing lengthy dialogues.
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