The Language of Bargaining: Linguistic Effects in LLM Negotiations

2026-01-07

arXiv.org

AI Summary

This study addresses the longstanding limitation in evaluating large language models’ (LLMs’) negotiation capabilities exclusively through English, thereby overlooking the influence of language and culture on negotiation behavior. By employing multi-agent simulations across Ultimatum, Buy-Sell, and resource-exchange bargaining tasks—while holding model parameters, incentives, and rules constant—the work systematically compares LLM performance in English versus four Indian languages: Hindi, Punjabi, Gujarati, and Marwari. The findings reveal, for the first time, that language choice can exert a stronger impact on negotiation outcomes than model differences themselves, with effects varying by task type: Indian languages reduce stability in distributive negotiations yet enhance strategic exploration in integrative settings, even reversing proposer advantage and reallocating surplus. These results challenge the prevailing English-centric evaluation paradigm in AI negotiation research.

Technology Category

• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms. Recent work shows that LLMs can engage in multi-turn negotiation, yet nearly all evaluations occur exclusively in English. Using controlled multi-agent simulations across Ultimatum, Buy-Sell, and Resource Exchange games, we systematically isolate language effects across English and four Indic framings (Hindi, Punjabi, Gujarati, Marwadi) by holding game rules, model parameters, and incentives constant across all conditions. We find that language choice can shift outcomes more strongly than changing models, reversing proposer advantages and reallocating surplus. Crucially, effects are task-contingent: Indic languages reduce stability in distributive games yet induce richer exploration in integrative settings. Our results demonstrate that evaluating LLM negotiation solely in English yields incomplete and potentially misleading conclusions. These findings caution against English-only evaluation of LLMs and suggest that culturally-aware evaluation is essential for fair deployment.

Problem

• LLM negotiation
• language effects
• cross-lingual evaluation
• social intelligence
• cultural bias

Innovation

• language effects
• LLM negotiation
• multilingual evaluation
• cultural awareness
• multi-agent simulation