# Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations

arXiv: https://arxiv.org/abs/2405.20195 · HTML: https://mlsanity.com/paper/2405.20195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 3 of 3)

- [AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions](https://mlsanity.com/paper/2602.06008/) (2026)
- [Multi-User Large Language Model Agents](https://mlsanity.com/paper/2604.08567/) (2026)
- [Generating Place-Based Compromises Between Two Points of View](https://mlsanity.com/paper/2604.24536/) (2026)

## Abstract (arXiv)

Humanitarian negotiations in conflict zones, called frontline negotiation, are often highly adversarial, complex, and high-risk. Several best-practices have emerged over the years that help negotiators extract insights from large datasets to navigate nuanced and rapidly evolving scenarios. Recent advances in large language models (LLMs) have sparked interest in the potential for AI to aid decision making in frontline negotiation. Through in-depth interviews with 13 experienced frontline negotiators, we identified their needs for AI-assisted case analysis and creativity support, as well as concerns surrounding confidentiality and model bias. We further explored the potential for AI augmentation of three standard tools used in frontline negotiation planning. We evaluated the quality and stability of our ChatGPT-based negotiation tools in the context of two real cases. Our findings highlight the potential for LLMs to enhance humanitarian negotiations and underscore the need for careful ethical and practical considerations.