Self-Emotion Blended Dialogue Generation in Social Simulation Agents
<|reference_start|>Self-Emotion Blended Dialogue Generation in Social Simulation Agents: 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.<|reference_end|>
@article{zhang2024self-emotion,
    title={Self-Emotion Blended Dialogue Generation in Social Simulation Agents},
    author={Qiang Zhang, Jason Naradowsky, Yusuke Miyao},
    journal={arXiv preprint arXiv:2408.01633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01633},
    primaryClass={cs.MA cs.AI cs.CL cs.CY}
}