Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues
Abstract: We develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business negotiations.
Specifically, we simulate business negotiations by letting two LLM-based agents engage in role play.
A third LLM acts as a remediator agent to rewrite utterances violating norms for improving negotiation outcomes.
We introduce a simple tuning-free and label-free In-Context Learning (ICL) method to identify high-quality ICL examples for the remediator, where we propose a novel select criteria, called \textit{value impact}, to measure the quality of the negotiation outcomes.
We provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different negotiation topics. The source code and the generated dataset will be publicly available upon acceptance.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: sociolinguistics, conversational modeling
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English, Chinese
Submission Number: 4536
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