Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
This work addresses the problem of improving chatbot empathy and efficiency for applications like customer support and mental health care, representing an incremental advancement.
The study tackled the challenges of chatbots understanding subtle nuances and managing long conversation histories by introducing Emotional Preference Optimization and MambaCompressor, resulting in significant outperformance over existing models in generating empathetic responses and efficiently managing lengthy dialogues.
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.