标题： 分解到M：通过模拟和任务分解增强大型语言模型的心智理论推理 显示英文标题

标题： Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language Models through Simulation and Task Decomposition

摘要： 心智理论（ToM）是指理解并反思他人心理状态的能力。 尽管这种能力对于人类互动至关重要，但对大型语言模型（LLMs）的测试表明，它们对此的理解仅限于基本层面。 尽管最先进的封闭源代码LLMs在某些ToM任务上已接近人类表现，但在涉及更结构化推理的复杂变体任务中，它们的表现仍然不佳。 在本工作中，我们利用认知心理学中的“假装游戏”或“模拟理论”概念，提出“Decompose-ToM”：一种基于LLM的推理算法，该算法提高了模型在复杂ToM任务上的性能。 我们递归地模拟用户视角，并将ToM任务分解为一组更简单的函数：主体识别、问题重构、世界模型更新和知识可用性。 我们在高阶ToM任务和一个测试对话环境中ToM能力的任务上测试了该算法，结果表明，与基线方法相比，我们的方法在不同模型上表现出显著的改进，同时在不同任务上只需最少的提示调整，且无需额外的模型训练。 显示英文摘要

摘要： Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close to human performance on some ToM tasks, they still perform poorly on complex variations of the task that involve more structured reasoning. In this work, we utilize the concept of "pretend-play", or ``Simulation Theory'' from cognitive psychology to propose ``Decompose-ToM'': an LLM-based inference algorithm that improves model performance on complex ToM tasks. We recursively simulate user perspectives and decompose the ToM task into a simpler set of functions: subject identification, question-reframing, world model updation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversational setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no additional model training.