Summary of Cause-aware Empathetic Response Generation Via Chain-of-thought Fine-tuning, by Xinhao Chen and Chong Yang and Man Lan and Li Cai and Yang Chen and Tu Hu and Xinlin Zhuang and Aimin Zhou
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning
by Xinhao Chen, Chong Yang, Man Lan, Li Cai, Yang Chen, Tu Hu, Xinlin Zhuang, Aimin Zhou
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 novel approach to empathetic response generation, which enables agents to comprehend dialogue contexts and react to expressed emotions. The method integrates emotions and causes through a Chain-of-Thought (CoT) prompt on Large Language Models (LLMs), promoting the LLMs’ performance of empathy by instruction tuning. The approach also incorporates cause-oriented external knowledge from COMET into the prompt, improving generation diversity and alleviating internal-external knowledge conflicts. Experimental results demonstrate state-of-the-art performance in both automatic and human evaluations on the benchmark dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand emotions and respond to people’s feelings better. Right now, most computer models focus on what people are feeling, but they don’t think about why those people are feeling that way. This makes it hard for computers to really understand how people are feeling and make good responses. The researchers in this paper came up with a new idea called Chain-of-Thought (CoT) that helps computers understand emotions better by thinking about the reasons behind them. They also added some extra information from COMET, which made their computer model even better at responding to emotions. |
Keywords
» Artificial intelligence » Instruction tuning » Prompt
