标题： 零、有限和无限信念历史的心智理论推理在大型语言模型中 显示英文标题

标题： Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models

摘要： 大型语言模型（LLMs）最近显示出心智理论（ToM）能力的潜力和出现，并且在某些ToM任务中甚至超过了人类。为了评估和扩展LLMs的ToM推理能力的边界，我们提出了一种新的概念、分类和框架，即基于零、有限和无限信念历史的ToM推理，并开发了一个多轮文本游戏，称为$\textit{Pick the Right Stuff}$，作为基准测试。我们用这个游戏评估了六个LLMs，并发现它们在零信念历史上的表现始终优于有限信念历史。此外，我们发现其中两个参数规模较小的模型超越了所有参数规模较大的评估模型。我们期望这项工作能为未来的ToM基准开发铺平道路，同时也促进需要具备更复杂ToM推理能力的更复杂的AI代理或系统的推广和发展。 显示英文摘要

摘要： Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the boundaries of the ToM reasoning ability of LLMs, we propose a novel concept, taxonomy, and framework, the ToM reasoning with Zero, Finite, and Infinite Belief History and develop a multi-round text-based game, called $\textit{Pick the Right Stuff}$, as a benchmark. We have evaluated six LLMs with this game and found their performance on Zero Belief History is consistently better than on Finite Belief History. In addition, we have found two of the models with small parameter sizes outperform all the evaluated models with large parameter sizes. We expect this work to pave the way for future ToM benchmark development and also for the promotion and development of more complex AI agents or systems which are required to be equipped with more complex ToM reasoning ability.