使用情感偏好优化和MambaCompressor在音视频对话中实现共情响应
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
AI总结：
针对聊天机器人理解微妙情感差异与管理长对话历史的挑战，提出结合情感偏好优化（EPO）与MambaCompressor的双重方法，显著提升了共情响应生成与长对话管理能力。
摘要：
随着聊天机器人在客户支持和心理健康护理等需要人类互动的领域中的重要性日益增加，聊天机器人研究正在不断推进。尽管取得了这些进展，聊天机器人在理解微妙差异和管理长对话历史方面仍面临重大挑战。为了解决这些问题，我们的研究引入了一种双重方法：首先，我们采用情感偏好优化（EPO）来训练聊天机器人，不仅使用正确的响应，还使用反情感响应——即在语境上相似但在情感上存在分歧的响应。这种训练使模型能够辨别正确响应与反情感响应之间的细微差异，从而提高其响应质量。其次，我们引入了MambaCompressor来有效压缩和管理广泛的对话历史，在显著降低时间和内存复杂度的同时，提高了聊天机器人对上下文的理解。我们在多个数据集上进行的综合实验表明，我们的模型在生成共情响应和有效管理冗长对话方面显著优于现有模型。
英文摘要：
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.
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。
