@inproceedings{jafari-etal-2025-beyond,
    title = "Beyond Words: Integrating Theory of Mind into Conversational Agents for Human-Like Belief, Desire, and Intention Alignment",
    author = "Jafari, Mehdi  and
      Hua, Yuncheng  and
      Xue, Hao  and
      Salim, Flora D.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.287/",
    pages = "5489--5508",
    ISBN = "979-8-89176-256-5",
    abstract = "Natural language interaction has long served as the primary medium through which humans exchange ideas. A key enabler of this communication is the human capacity for Theory of Mind (ToM){---}the ability to infer and align with the mental states of others. ToM is usually modeled as components of desires, beliefs, and intentions. Research in linguistics and psychology has shown that people oftentimes reveal their ToM through pragmatic aspects of language. Considering the advancements in natural language generation and perception that Large Language Models (LLMs) have made in recent years, a critical question arises in relation to ToM: can LLM-powered agents develop similar abilities for inferring mental states during natural language communication? This study investigates the extent to which open-source LLaMA models can represent and retain ToM-related constructs, and whether these internal representations contribute to a coherent mental state modeling in a given conversation. Additionally, we explore the potential for manipulating ToM-related information to generate more aligned responses. Empirical evaluations of LLaMA-3 models (3B and 8B) demonstrate that ToM-informed alignment improves response quality, achieving win rates of 63{\%} and 67{\%}, respectively. These findings suggest that integrating ToM principles can enhance alignment in LLM-based conversational agents. For further details, refer to the [code repository](https://github.com/cruiseresearchgroup/ToM{\_}and{\_}Alignment)."
}