Toward a Better Understanding of the Emotional Dynamics of Negotiation with Large Language Models
Toward a Better Understanding of the Emotional Dynamics of Negotiation with Large Language Models
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DOI：
10.1145/3565287.3617637
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发表时间：
2023-10
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通讯作者：
Eleanor Lin;James Hale;J. Gratch
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作者：
Eleanor Lin;James Hale;J. Gratch
目前构建谈判代理的方法要么依赖于明确实现谈判关键原则的基于模型的技术，要么依赖于利用通过对大量人类生成文本进行训练而开发的算法的无模型技术。我们通过将基于模型的方法与用于自然语言理解和生成的大型语言模型相结合来连接这两种方法。我们发现大型语言模型在识别对话行为和对手的情绪方面表现良好；在谈判中能够很好地识别对手的偏好；并且在理解对手报价方面表现较差。我们还对混合方法与无模型方法的能力进行了定性比较，发现我们的混合代理提供了防止幻觉的保障，并保证了对谈判方面的更多控制，例如情绪表达、信息共享和让步策略。
Current approaches to building negotiation agents rely either on model-based techniques that explicitly implement key principles of negotiation or model-free techniques leveraging algorithms developed via training on large amounts of human-generated text. We bridge these two approaches by combining a model-based approach with large language models for natural language understanding and generation. We find large language models perform well at recognizing dialogue acts and an opponent's emotions; perform reasonably well at recognizing opponents' preferences in the negotiation; and perform worse at understanding opponent offers. We also perform a qualitative comparison of the capabilities of our hybrid approach with a model-free method and find our hybrid agent provides safeguards against hallucinations and guarantees more control over aspects of negotiation such as emotional expressions, information sharing, and concession strategies.