@inproceedings{shinoda-etal-2026-lets,
    title = "Let{'}s Put Ourselves in Sally{'}s Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models",
    author = "Shinoda, Kazutoshi  and
      Hojo, Nobukatsu  and
      Nishida, Kyosuke  and
      Yamazaki, Yoshihiro  and
      Suzuki, Keita  and
      Sugiyama, Hiroaki  and
      Saito, Kuniko",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.6/",
    pages = "95--109",
    ISBN = "979-8-89176-386-9",
    abstract = "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."
}