Recent studies have shown that Theory of Mind (ToM) in large language models (LLMs) has not reached human-level performance yet. Since fine-tuning LLMs on ToM datasets often degrades their generalization, several inference-time methods have been proposed to enhance ToM in LLMs. However, existing inference-time methods for ToM are specialized for inferring beliefs from contexts involving changes in the world state. In this study, we present a new inference-time method for ToM, Shoes-of-Others (SoO) prefilling, which makes fewer assumptions about contexts and is applicable to broader scenarios. SoO prefilling simply specifies the beginning of LLM outputs with ``Let's put ourselves in A's shoes.'', where A denotes the target character's name. We evaluate SoO prefilling on two benchmarks that assess ToM in conversational and narrative contexts without changes in the world state and find that it consistently improves ToM across five categories of mental states. Our analysis suggests that SoO prefilling elicits faithful thoughts, thereby improving the ToM performance.

翻译：近期研究表明，大语言模型的心理理论能力尚未达到人类水平。由于在心理理论数据集上微调大语言模型通常会损害其泛化能力，学界已提出若干推理时方法来增强模型的心理理论能力。然而，现有心理理论推理方法主要专注于从涉及世界状态变化的语境中推断信念。本研究提出一种新的心理理论推理方法——他人视角预填充，该方法对语境的假设更少，适用于更广泛的场景。他人视角预填充只需在大语言模型输出起始处指定"让我们设身处地为A着想"，其中A表示目标角色名称。我们在两个评估对话和叙事语境中心理理论能力的基准测试上验证了该方法，这些测试不涉及世界状态变化。实验表明，该方法在五类心理状态上持续提升了心理理论能力。分析表明，他人视角预填充能够引发忠实的思想表达，从而提升心理理论表现。