不同语言显著影响大模型谈判结果，甚至逆转优势。
The Language of Bargaining: Linguistic Effects in LLM Negotiations
- 在多轮谈判中控制变量，对比英语与四种印度语的博弈表现。
- 使用印地语等语言时，提案方优势反转，收益分配发生改变。
- 语言影响取决于任务类型，宜用于跨文化智能评估。
谈判是社交智能的核心，要求代理在策略推理、合作与社会规范间取得平衡。近期研究显示大模型可进行多轮谈判，但几乎所有评估均局限于英语。通过在最后通牒、买卖和资源交换游戏中开展受控多智能体模拟，我们系统性地在英语及四种印度语（印地语、旁遮普语、古吉拉特语、马瓦迪语）框架下隔离语言影响，保持游戏规则、模型参数和激励不变。结果发现，语言选择对结果的影响甚至超过更换模型，能逆转提案方优势并重新分配剩余收益。关键的是，这些效应具有任务依赖性：在分配型游戏中，印度语降低稳定性；而在整合型场景中则促进更丰富的探索。研究证明，仅以英语评估大模型谈判会导致不完整甚至误导性结论。这警示应避免单一语言评估，倡导在公平部署中引入文化敏感性评估。
原文摘要 · Abstract (English)
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.
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