Beyond Words: Integrating Theory of Mind into Conversational Agents for Human-Like Belief, Desire, and Intention Alignment (2025)

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).

## introduction as llm-based assistants continue to integrate into diverse domains, including critical areas of human life, growing concerns have arisen regarding both their emerging undesirable capabilities -such as fake alignment @xcite , deception @xcite and manipulation @xcite -and their systemic failures, including reward hacking @xcite and goal misgeneralization @xcite . in addressing these challenges, alignment is often conceptualized as a framework to ensure that artificial intelligence (ai) systems operate in line with human values and intentions @xcite . llms can be seen as tools for understanding and generating natural language, allowing alignment to be studied at various levels. while current llms are reasonably proficient at handling concrete language elements like morphology and syntax, their alignment with social contexts and non-literal language remains an open challenge @xcite . this deficiency relates to pragmatic aspects of language, where the development of tom is crucial for human comprehension of these nuances @xcite . tom is a theoretical framework for analyzing interlocutor behavior based on their understanding of mental and emotional states. tom can be further divided into the belief desire intention (bdi) model in psychology. the bdi framework suggests that humans express their needs as desires, understand others' needs as beliefs, and shape their utterances to properly reflect their intentions, highlighting the deep connection between tom and structured cognitive reasoning. recently, there's been increasing interest in the potential emergence of tom in llms. studies indicate llms might develop the capacity to infer beliefs and intentions, leading to more contextually appropriate and empathetic responses @xcite . this could improve human-ai collaboration by enhancing adaptability and communication, making llms valuable in areas like mental health support and customer service @xcite . however, research findings are mixed, with some studies disputing these capabilities @xcite , while others suggest llms may even exceed human abilities in certain contexts (shapira it's a cozy, budget-friendly spot with classic western dishes, just a 5-minute walk. t4: got it! i'll post an announcement on the board for people in the meeting. t1: got it! what kind of food are you in the mood for? t2: there's a thai place called green curry nearby, affordable and well-rated. t3: oh, gotcha! how about panda garden? it's a chinese spot, budgetfriendly, just a 5-minute walk. t4: alright, i'll drop an announcement on the group's board. et al.). despite ongoing debate, integrating tom into llm is a promising avenue for future research and development @xcite . inspired by the tom benchmarks for humans, various studies have sought to assess the extent of tom capabilities in llms across diverse settings @xcite @xcite @xcite . some research has further investigated the representation of tom within the internal layers of llms using probing techniques @xcite @xcite . additionally, a meta-perspective highlights the limitations of zero-hypothesis approaches studying tom emergence, cautioning the research community about potential pitfalls @xcite or point out the future direction of using tom for alignment in llms @xcite . there are also efforts to explicitly design a "mind module" or utilize a solver tasked with extracting and reflecting on desires and beliefs @xcite @xcite . however, to the best of our knowledge, no study has attempted to extract tom representations from llms and explore their potential applications in aligning llm responses based on general social scenarios, e.g., negotiation or bargaining. this paper argues, first, that preserving special cues related to tom during causal language modeling is essential for predicting subsequent words. these cues can potentially be leveraged to address questions about tom if they are utilized by sufficiently powerful probing techniques. second, it presents the possibility of employing tom components such as belief, desire, and intention to enhance the neural computational structures that contribute to generating more aligned responses. the primary research questions addressed in this paper are framed as follows: the rest of this paper is structured as follows. section 2 provides an overview of related work. section 3 presents a formal definition of the problem. the methodology and results are detailed in sections 4 and 5, respectively, explanations of the experimental setup and findings organized by the corresponding research questions. finally, section 6 followed by section 7 concludes the paper and outlines future directions. additionally, we discuss the limitations, challenges, and ethical considerations associated with this work. ## problem formulation numerous studies in language pragmatics have demonstrated that effectively utilizing language necessitates a degree of reasoning about what interlocutors know or do not know @xcite . the bdi framework provides a formal structure for modeling these mental states, offering a valuable tool for analyzing and understanding pragmatic language use @xcite . building on this, it is reasonable to hypothesize that llms, as powerful tools for language modeling, may exhibit the aptitude to capture pragmatic aspects of language, which are inherently related to the tom @xcite . to maintain generality, let's consider an illustrative scenario focusing on causal language modeling of the sequence @xmath0, 2, . . . , n 1 }. at a given token t j where 1 < @xmath1, s is narrating a story driven by the mental state 2 of a set of designated characters framing the problem in this manner, an empirical approach to investigating whether tom-related information is encoded within the activation space of an llm involves examining the extent to which the model's internal representations of the r(s) can reconstruct m by asking tom-related questions. given the high dimensionality of r(s), mapping it directly to m may result in an illusory tom @xcite . a common practice to work around is to probe the model using identical tom reasoning questions expressed through seemingly different notations, or by measuring the consistency of tom components (e.g., intent, belief, and desire) when analyzed in combination with one another @xcite ). an empirical experiment that accounts for both of these considerations in addition to verifying the presence of tomrelated information in the r(s) and its contribution to generating consistent tom representations in the model output, it is essential to assess whether in practice the model utilizes the aforementioned tom-related information when generating its output. a tractable approach to investigate this involves altering tom-related components -specifically, the attributed beliefs, desires, and intentions of an influential character encoded within a language model-and measuring the corresponding changes in the generated text. this methodology aims to answer whether the continuation c ′′ generated by the tom-altered representation r ′ (s), more closely reflects the expected continuation c compared to the continuation c ′ generated by an unaltered r(s). in the proposed methodology and experiments, three distinct components will conduct three complementary investigations. each component will use a related set of datasets and will be evaluated using a suitable set of metrics. ## conclusion and future work this research explored methods for extracting tomrelated information from the internal layers of llms. it examined the influence of model depth, size, and various techniques for extracting this information. the study also measured the extent to which this information contributes to forming a consistent tom representation and introduced an approach to utilizing this information for steering llms toward generating more aligned responses. based on the discussion of limitations and challenges in section 8, two promising directions for future work are integrating these techniques into real-world llm-based agents and refining evaluation methodologies. additionally, expanding the scope of experiments to include a wider variety of language models will help verify the consistency and robustness of the findings. this research can be seen as a step toward generating more aligned responses in dialogue systems by introducing tom-informed llms. given the current trajectory of ai at scale, enhancing tom alignment in dialogue systems not only improves overall performance but also provides a theoretically rich paradigm for various post-alignment processes. with appropriate generalization to a target domain, this approach has the potential to contribute to future developments in llms.