MERIT反馈促进LLM谈判者更好的协商
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
AI总结：
MERIT反馈框架通过AgoraBench基准、经济基础指标和人类偏好数据集提升LLM谈判能力，使其更符合人类偏好。
摘要：
协商常被视为一个逻辑领域而非艺术或直觉问题，但大型语言模型（LLMs）仍因战略深度有限和难以适应复杂的人类因素而难以应对。当前的基准测试很少捕捉这一限制。为弥合这一差距，我们提出了一种以效用反馈为中心的框架。我们的贡献包括：（i）AgoraBench，一个涵盖九种挑战性场景（如欺骗、垄断）的新基准，支持多样化策略建模；（ii）基于效用理论的人类对齐、经济基础的指标。这通过代理效用、谈判力量和获取比率等指标隐式衡量谈判与人类偏好的一致性；（iii）一个基于人类偏好的数据集和学习流程，通过提示和微调增强LLM的协商能力。实证结果表明，基线LLM策略常偏离人类偏好，而我们的机制显著提高了谈判表现，产生更深层次的战略行为和更强的对手意识。
英文摘要：
Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic depth and difficulty adapting to complex human factors. Current benchmarks rarely capture this limitation. To bridge this gap, we present a utility feedback centric framework. Our contributions are: (i) AgoraBench, a new benchmark spanning nine challenging settings (e.g., deception, monopoly) that supports diverse strategy modeling; (ii) human-aligned, economically grounded metrics derived from utility theory. This is operationalized via agent utility, negotiation power, and acquisition ratio that implicitly measure how well the negotiation aligns with human preference and (iii) a human preference grounded dataset with learning pipeline that strengthens LLMs' bargaining ability through both prompting and finetuning. Empirical results indicate that baseline LLM strategies often diverge from human preferences, while our mechanism substantially improves negotiation performance, yielding deeper strategic behavior and stronger opponent awareness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
