Toward a Better Understanding of the Emotional Dynamics of Negotiation with Large Language Models

Eleanor M Lin, James Anthony Hale, Jonathan Gratch

Abstract:

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.