The Language of Bargaining: Linguistic Effects in LLM Negotiations

ACL ARR 2026 January Submission6663 Authors

Keywords: Large Language Models, Negotiation, Multilingual Evaluation, Linguistic Framing, Cross-lingual Performance, Cultural Bias, Indic Languages, Strategic Behavior, Social AI

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.

Paper Type: Long

Research Area: Multilinguality and Language Diversity

Research Area Keywords: multilingual evaluation, cross-lingual transfer, multilingual benchmarks, linguistic variation, less-resourced languages

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English, Hindi, Marwadi, Punjabi, Gujarati

Submission Number: 6663