Self-Emotion Blended Dialogue Generation in Social Simulation Agents
- URL: http://arxiv.org/abs/2408.01633v1
- Date: Sat, 3 Aug 2024 02:11:48 GMT
- Title: Self-Emotion Blended Dialogue Generation in Social Simulation Agents
- Authors: Qiang Zhang, Jason Naradowsky, Yusuke Miyao,
- Abstract summary: This study explores how self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework.
The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies.
In a virtual simulation environment where agents have discussions on multiple topics, we show that self-emotion of agents can significantly influence the decision-making process of the agents.
- Score: 18.781929161272853
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: When engaging in conversations, dialogue agents in a virtual simulation environment may exhibit their own emotional states that are unrelated to the immediate conversational context, a phenomenon known as self-emotion. This study explores how such self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework. In a dialogue strategy prediction experiment, we analyze the dialogue strategy choices employed by agents both with and without self-emotion, comparing them to those of humans. The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies. In an independent experiment comparing the performance of models fine-tuned on GPT-4 generated dialogue datasets, we demonstrate that self-emotion can lead to better overall naturalness and humanness. Finally, in a virtual simulation environment where agents have discussions on multiple topics, we show that self-emotion of agents can significantly influence the decision-making process of the agents, leading to approximately a 50% change in decisions.
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