Practical mechanisms often limit agent reports to constrained formats like trades or orderings, potentially limiting the information agents can express. We propose a novel class of mechanisms that elicit agent reports in natural language and leverage the world-modeling capabilities of large language models (LLMs) to select outcomes and assign payoffs. We identify sufficient conditions for these mechanisms to be incentive-compatible and efficient as the LLM being a good enough world model and a strong inter-agent information over-determination condition. We show situations where these LM-based mechanisms can successfully aggregate information in signal structures on which prediction markets fail.

翻译：实际应用中的机制通常将代理报告限制在交易或排序等约束格式内，这可能限制代理所能表达的信息。我们提出一类新颖的机制，其通过自然语言收集代理报告，并利用大语言模型（LLMs）的世界建模能力来选择结果并分配收益。我们确定了这类机制具备激励相容性与效率的充分条件：LLM需具备足够精确的世界建模能力，并满足强化的代理间信息超定条件。我们证明了在某些信号结构场景下，此类基于语言模型的机制能够成功聚合信息，而传统预测市场在这些场景中则会失效。