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 exemplars for the remediator, where we propose a novel select criteria, called 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. We have released our source code and the generated dataset at: https://github.com/tk1363704/SADAS.- Anthology ID:
- 2024.findings-emnlp.473
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8047–8074
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.473/
- DOI:
- 10.18653/v1/2024.findings-emnlp.473
- Cite (ACL):
- Yuncheng Hua, Lizhen Qu, and Reza Haf. 2024. Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8047–8074, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues (Hua et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/2024.findings-emnlp.473.pdf