Summary of Empathetic Response in Audio-visual Conversations Using Emotion Preference Optimization and Mambacompressor, by Yeonju Kim et al.
Empathetic Response in Audio-Visual Conversations Using Emotion Preference Optimization and MambaCompressor
by Yeonju Kim, Se Jin Park, Yong Man Ro
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a dual approach to enhance the capabilities of chatbots in handling subtle nuances and long conversation histories. The first component, Emotional Preference Optimization (EPO), trains chatbots not only with correct responses but also with counter-emotional responses that are contextually similar yet emotionally divergent. This enables the model to discern fine nuance distinctions between correct and counter-emotional responses, improving response quality. The second component, MambaCompressor, efficiently compresses and manages extensive conversation histories, reducing time and memory complexities while enhancing contextual understanding. Experimental results demonstrate that this approach outperforms existing models in generating empathetic responses and managing lengthy dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chatbots are getting smarter at helping people with tasks like customer support and mental health care. But they still struggle to understand some important details and remember long conversations. This paper tries to fix these problems by coming up with a new way to train chatbots that involves giving them both the correct answer and an opposite emotional response. This helps the chatbot figure out subtle differences between answers and respond more empathetically. The team also created a tool called MambaCompressor that makes it easier for chatbots to remember long conversations without getting overwhelmed. By testing their approach on multiple datasets, they showed that their method does better than existing methods at generating helpful responses and handling long conversations. |
Keywords
» Artificial intelligence » Optimization
