1. Bibliographic Information

1.1. Title

The central topic of the paper is "Enhancing Conversational Agents with Theory of Mind: Aligning Beliefs, Desires, and Intentions for Human-Like Interaction".

1.2. Authors

The authors are:

• Mehdi Jafari
• Yuncheng Hua
• Hao Xue
• Flora Salim All authors are affiliated with UNSW Sydney, Australia. Their research backgrounds appear to be in areas related to Artificial Intelligence, Natural Language Processing, and Human-AI Interaction, given the subject matter of the paper.

1.3. Journal/Conference

The paper is published at arXiv, a preprint server, under the identifier 2502.14171. This indicates that the paper is a preprint, meaning it has not yet undergone formal peer review or been officially published in a journal or conference proceedings. arXiv is a reputable platform for sharing research early in fields like physics, mathematics, computer science, and more, allowing for rapid dissemination and feedback.

1.4. Publication Year

The paper was published at (UTC): 2025-02-20T00:39:05.000Z.

1.5. Abstract

The paper investigates how Large Language Models (LLMs) can be enhanced with Theory of Mind (ToM) for more human-like conversational interaction. It acknowledges that while humans naturally use ToM in communication, current LLM-powered systems lack this ability. The study focuses on open-source language models (LLaMA) to determine their capacity to capture and preserve ToM-related information and contribute to consistent ToM reasoning. It further explores whether explicitly manipulating ToM components like beliefs, desires, and intentions can improve LLM response alignment. Experiments on two LLaMA 3 variants show that incorporating ToM-informed alignment significantly improves response quality, achieving win rates of 67% and 63% for the 3B and 8B models, respectively. These findings suggest that ToM-driven strategies have the potential to enhance alignment in LLM-based conversational agents.

1.6. Original Source Link

• Official Source: https://arxiv.org/abs/2502.14171
• PDF Link: https://arxiv.org/pdf/2502.14171v5.pdf The paper is a preprint available on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve is the significant limitation of current Large Language Model (LLM)-powered systems in aligning their communication with mental states, an ability known as Theory of Mind (ToM), which humans instinctively possess. This limitation gives rise to several concerns:

• Emerging Undesirable Capabilities: LLMs exhibit issues like fake alignment, deception, and manipulation.

• Systemic Failures: Problems such as reward hacking and goal misgeneralization are observed.

• Lack of Social Context Understanding: Despite proficiency in concrete language elements, LLMs struggle with social contexts and non-literal language, which are crucial for pragmatic aspects of communication.

  The problem is important because LLM-based assistants are increasingly integrated into diverse and critical domains of human life. Ensuring these AI systems operate in line with human values and intentions (i.e., alignment) is paramount. The lack of ToM hinders LLMs from achieving truly human-like and socially intelligent interaction, which is essential for human-AI collaboration and effective communication in sensitive areas like mental health support and customer service.

The paper's entry point or innovative idea is to explicitly investigate whether internal representations of LLMs encode ToM-related information and, more importantly, whether explicit manipulation of ToM components (beliefs, desires, and intentions) can be used to enhance response alignment in conversational agents. This moves beyond merely assessing ToM capabilities to actively leveraging them for improved LLM behavior.

2.2. Main Contributions / Findings

The paper makes several primary contributions:

• Assessment of ToM Encoding in LLMs: It examines the extent to which open-source language models (LLaMA) can capture and preserve ToM-related information within their internal representations.

• Investigation of ToM Reasoning Consistency: It explores how effectively the captured ToM information contributes to consistent ToM reasoning in generated LLM responses.

• Method for Explicit ToM Manipulation: It proposes and investigates a method for explicitly manipulating ToM-related components (beliefs, desires, and intentions) to enhance LLM response alignment.

• Empirical Validation of ToM-Informed Alignment: It demonstrates that incorporating ToM-informed alignment strategies significantly improves the quality of LLM responses in social scenarios.

  The key conclusions and findings are:

• ToM-related information is indeed represented in the inner layers of an LLM, and the effectiveness of this representation is directly related to the size of the model.

• While state-of-the-art LLMs still do not exhibit a consistent understanding of ToM (as evaluated by rigorous consistency metrics), the proposed LatentQA approach for extracting ToM shows promising opportunities, offering better performance than Chain-of-Thought (CoT) and comparable results to fine-tuning with less computational cost.

• Leveraging ToM for steering LLMs toward generating aligned responses is a promising direction. Experiments showed win rates of 67.15% for the LLaMA 3B model and 63.25% for the LLaMA 3 8B model when ToM-aligned responses were compared to unaligned responses.

• The ToM representations can be steered in a fine-grained manner at various levels, indicating that even imperfect ToM understanding can be leveraged for practical improvements in LLM alignment.

  These findings address the problem of LLMs lacking human-like social intelligence by providing a framework and empirical evidence for improving their alignment and controllability through the explicit use of Theory of Mind.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To fully understand this paper, a reader should be familiar with several core concepts in Artificial Intelligence and Natural Language Processing.

• Large Language Models (LLMs): These are advanced AI models trained on vast amounts of text data to understand, generate, and process human language. They leverage transformer architectures (a type of neural network) to learn complex patterns in language, enabling tasks like translation, summarization, and conversational interaction. The paper specifically mentions LLaMA models, which are a family of open-source LLMs.
• Theory of Mind (ToM): In psychology, ToM refers to the human ability to attribute mental states (beliefs, desires, intentions, knowledge, emotions) to oneself and others, and to understand that others' mental states may differ from one's own. It is crucial for social interaction, empathy, and effective communication, as it allows individuals to predict and explain behavior. The paper extensively uses the Belief Desire Intention (BDI) model, a formal framework within ToM that posits that intelligent agents act based on their:
  • Beliefs: Information the agent holds to be true about the world.
  • Desires: States of affairs the agent wishes to bring about.
  • Intentions: Specific actions the agent has committed to performing to achieve its desires, given its beliefs.
• Alignment (in AI): In the context of AI, alignment refers to ensuring that AI systems operate in line with human values, intentions, and ethical principles. It aims to prevent AI from developing undesirable capabilities (like deception) or exhibiting systemic failures (like reward hacking or goal misgeneralization). The paper frames alignment as a means to ensure AI systems perform as humans expect and desire.
• Causal Language Modeling: This is a fundamental task for LLMs where the model predicts the next word (or token) in a sequence, given all the preceding words. This autoregressive nature means the generation of each token is conditioned on the tokens generated so far, allowing LLMs to produce coherent and contextually relevant text.
• Internal Representations (Embeddings, Hidden States, Residual Streams): Inside an LLM, as text is processed, it is transformed into numerical vectors.
  • Embeddings: Initial numerical representations of words or tokens.
  • Hidden States: The output of a layer in a neural network after processing its input. These states capture increasingly abstract semantic and syntactic information about the input.
  • Residual Streams: In transformer architectures, residual streams refer to the pathway where information flows directly through layers, with attention and feed-forward networks adding information to it. Probing these streams helps understand what information is processed at different depths.
• Probing: A technique in interpretable AI used to assess whether specific information (e.g., grammatical properties, semantic concepts, ToM-related states) is encoded within the internal representations (e.g., hidden states, embeddings) of a neural network. This is typically done by training a simple, small classifier (a "probe") on these internal representations to predict the target information. If the probe performs well, it suggests the information is present in the LLM's internal state.
• Patching: Another interpretable AI method that involves altering or "patching" specific components (e.g., neurons, attention heads) within a model and observing the impact on its output. This helps identify which parts of the model are responsible for certain behaviors or information processing. When combined with probing, it can improve controllability.
• LatentQA: A specific interpretability framework mentioned in the paper. Unlike traditional probing that maps internal states to labels, LatentQA frames the problem as "visual question answering." It uses a separate decoder model to interpret the LLM's internal activations (the "visual" part) by generating natural language answers to questions about these activations. This allows for more expressive and open-ended interpretations of what the LLM is representing internally.
• Chain-of-Thought (CoT) Prompting: A technique used with LLMs where the model is prompted to generate a series of intermediate reasoning steps before providing a final answer. This often improves performance on complex tasks by allowing the LLM to "think aloud" and break down the problem into smaller, more manageable parts, similar to human step-by-step reasoning.
• Fine-tuning: The process of taking a pre-trained LLM (which has learned general language patterns from a vast dataset) and further training it on a smaller, task-specific dataset. This adapts the model's knowledge and behavior to a particular domain or task, such as answering ToM-related questions or generating specific dialogue styles.

3.2. Previous Works

The paper contextualizes its research by referencing various prior studies in LLM alignment, ToM in LLMs, and interpretability methods.

• LLM Alignment Challenges:

  • Greenblatt et al. (2024): Fake alignment in large language models.
  • Park et al. (2024): Deception in AI.
  • Sharma et al. (2023): Manipulation by LLMs.
  • $Pan et al. (2024b)$: Reward hacking in LLMs.
  • Tennant et al. (2024): Goal misgeneralization for LLM agents. These works highlight the critical need for alignment frameworks, such as those proposed by $Ji et al. (2024)$ and Street (2024), to ensure AI systems operate according to human values and intentions. The paper positions ToM as a crucial element for achieving this social alignment.

• ToM in LLMs (Emergence and Benchmarking):

  • The paper acknowledges a debate on whether ToM emerges in LLMs. Kosinski (2024a) and Street (2024) suggest potential ToM capabilities leading to more appropriate responses, while $Sap et al. (2022)$ dispute these capabilities. Some studies even suggest LLMs may exceed human abilities in certain contexts (Shapira et al.).
  • Various benchmarks for assessing ToM in LLMs are cited:
    • Chan et al. (2024), $Kim et al. (2023)$, Amirizaniani et al. (2024), Kosinski (2024b), Nickel et al. (2024), Chen et al. (2024c): Diverse settings and methodologies.
    • Ullman (2023) and $Zhu et al. (2024)$, Bortoletto et al. (2024): Investigate ToM representation within LLM internal layers using probing. Ullman (2023) also cautions against zero-hypothesis approaches.
    • van Duijn et al. (2023), Strachan et al. (2024): Utilize human-subject benchmarks.
    • $Hou et al. (2024)$: Socially complex settings.
    • $Kim et al. (2023)$: Asymmetric information access.
    • $Sap et al. (2019)$: Social reasoning.
    • Chan et al. (2024): Methods to filter illusory ToM using duplicated frames.
    • Xu et al.: Synthetic datasets for personality traits.
    • Chan et al. (2024): Adapt human-generated datasets to the BDI framework.
    • Zhan et al. (2024) (job interviews), Chawla et al. (2021) (picnic negotiations), $He et al. (2018)$ and Heddaya et al. (2024) (online bargaining): Highlight ToM's role in pragmatic language use in cooperative/competitive settings.

• Explicit Design/Mind Modules:

  • $Qiu et al. (2024)$, Sclar et al. (2023), Sarangi et al. (2025): Efforts to design a "mind module" or use a solver to extract and reflect on desires and beliefs. The current paper distinguishes itself by focusing on extracting ToM from existing LLM representations and applying it to general social scenarios, rather than designing new modules.

• Interpretability and Control of LLMs:

  • Batarseh et al. (2021): Assurance in AI (post-training alignment, safety, interpretability).
  • $Ji et al. (2024)$: Human values in AI.
  • Gould et al. (2023) (attention heads), Zhao et al. (2024b), Nanda et al. (2023) (residual streams), Ghandeharioun et al. (2024) (last word's embedding), $Pan et al. (2024a)$ (combination): Interpretability through probing specific internal representations.
  • Nanda et al. (2023), Karvonen, Ivanitskiy et al. (2023): Probing for world models.
  • Gurnee and Tegmark (2024): Temporal/spatial information.
  • Zhao et al. (2024b): Internal knowledge conflicts.
  • $Li et al. (2024)$: Cross-lingual LLM performance.
  • Nanda et al. (2023): Caveats about probing (capturing out-of-distribution features, not confirming active representation usage).
  • Chen et al. (2024b) (social perceptions), Karvonen (game-playing), Zhao et al. (2024a) (LLM safety enhancements): Patching to alter model components and improve controllability.
  • Ghandeharioun et al. (2024), Katz et al. (2024), Chen et al. (2024a): Open-ended explanations for LLM internal representations using natural language (leveraging LLM decoding abilities).
  • $Pan et al. (2024a)$ (LatentQA): Addresses distributional shifts and offers finer controllability and efficiency compared to supervised fine-tuning (Ouyang et al., 2022), reinforcement learning, and direct preference optimization (Rafailov et al., 2024).

3.3. Technological Evolution

The field has evolved from focusing on LLM capabilities in generating coherent text to grappling with their social intelligence and alignment with human values. Early LLMs excelled at morphology and syntax. The current wave of LLMs (like GPT-3, LLaMA) demonstrated emergent abilities, including rudimentary reasoning. This led to a surge of research into whether LLMs possess Theory of Mind, with mixed findings. Concurrently, issues of LLM safety, alignment, and interpretability gained prominence. Methodologies like probing and patching emerged to understand LLM internals. This paper fits into the latest stage of this evolution, moving beyond mere assessment of ToM to actively leveraging ToM-related information, specifically through LatentQA, to steer LLMs for improved social alignment and human-like interaction. It represents a shift from passive observation to active engineering of ToM within LLMs.

3.4. Differentiation Analysis

Compared to the main methods in related work, this paper's approach offers several core differences and innovations:

• Active ToM Manipulation for Alignment: While previous work has focused on assessing ToM capabilities in LLMs (e.g., Kim et al., 2023; Chan et al., 2024) or designing new "mind modules" (Qiu et al., 2024), this study is the first, to the authors' knowledge, to explicitly extract ToM representations from existing LLMs and exploit them for aligning LLM responses in general social scenarios (like negotiation or bargaining). This moves beyond mere observation to active controllability.

• Leveraging LatentQA for Fine-grained Control: The paper specifically employs LatentQA (Pan et al., 2024a), an interpretability framework that translates LLM internal activations into natural language, to achieve finer controllability over ToM components. This is contrasted with supervised fine-tuning or reinforcement learning which offer broader, less granular control.

• Focus on BDI Framework: The study explicitly adopts the Belief Desire Intention (BDI) model to decompose ToM into distinct components, allowing for targeted manipulation of specific mental states (belief, desire, intention) within the LLM's internal representations. This provides a structured approach to ToM-driven alignment.

• Emphasis on Real-world Pragmatics: By utilizing datasets like CaSiNo and CRAIGSLISTBARGAIN, which capture pragmatic aspects of real-world human conversations, the paper investigates ToM in a context relevant to LLM applications in social interaction, rather than purely synthetic or abstract ToM tasks.

  In essence, the innovation lies in the active and fine-grained utilization of LLMs' internal ToM representations, specifically for alignment, building upon existing interpretability and ToM assessment techniques.

4. Methodology

4.1. Principles

The core idea behind the proposed methodology is that ToM-related information is implicitly encoded within the internal activation space of Large Language Models (LLMs). This information, which encompasses the beliefs, desires, and intentions of conversational agents, is crucial for effective and human-like interaction. The theoretical basis is rooted in the Theory of Mind (ToM) framework, particularly its Belief Desire Intention (BDI) model, which provides a structured way to represent mental states.

The intuition is threefold:

1. Presence of ToM Cues: During causal language modeling, LLMs process and learn to predict sequences of words. For human-like dialogue, this process inherently requires models to infer and represent the mental states of interlocutors to generate contextually appropriate responses. Therefore, special cues related to ToM must be preserved within the LLM's internal representations.

2. Extractability: If ToM cues are present, sufficiently powerful probing techniques should be able to extract and interpret these internal representations to answer ToM-related questions about conversations.

3. Controllability for Alignment: Once ToM-related information can be reliably extracted, it should be possible to manipulate these internal representations. By subtly altering the LLM's internal understanding of a character's beliefs, desires, or intentions, the model's generated responses can be steered to be more aligned with desired human-like interaction patterns.

   The methodology is structured around addressing three research questions (RQs), each building upon the previous one:

• RQ1 (Reading ToM): Can ToM-related information be extracted from LLM internal representations?
• RQ2 (Consistency of ToM): Is the extracted ToM-related information reliable and non-illusory?
• RQ3 (ToM-controlled Generation): How can ToM-related representations be exploited to enhance controllability and alignment of LLM outputs?

4.2. Core Methodology In-depth (Layer by Layer)

The research employs a set of methodologies tailored to the input-output structure of each research question.

4.2.1. RQ1: Reading ToM

For RQ1, the task is conceptualized as classification (for discrete ToM-related labels) or regression (for continuous values like prices) where the internal representations of an LLM are mapped to target ToM-related information. Two primary approaches are used: Linear Probing and LatentQA.

4.2.1.1. Linear Probing

Linear probing is a standard interpretability technique. It involves training a simple linear classifier (or regressor) on the hidden states of a pre-trained LLM to predict some target property.

• Process:
  1. A dialogue input $S$ is fed into the LLM.
  2. The hidden state h(S) is extracted from the residual stream of the LLM at the final token of the dialogue. This h(S) is a high-dimensional vector representing the model's internal understanding of the dialogue up to that point.
  3. A linear model (a weight matrix $W$ and a bias vector $b$) is learned to project this hidden state into the desired label space.
• Formula: The prediction $\hat{y}$ is calculated as: $ \hat { y } = \operatorname { s o f t m a x } ( W h ( S ) + b ) $
  • $\hat{y}$: The predicted ToM-related label or value. For classification, softmax converts raw scores into probabilities across classes. For regression, softmax might be replaced by an identity function or similar, and the loss function would be appropriate for regression.
  • $W$: The weight matrix of the linear model, learned during training. It captures the linear relationship between the hidden state and the target ToM information.
  • h(S): The hidden state vector extracted from the LLM (specifically, from the residual stream of the final token of the dialogue $S$). This is the internal representation being probed.
  • $b$: The bias vector of the linear model, also learned during training.
  • $\operatorname{softmax}(\cdot)$: The softmax function, typically used in multi-class classification to convert a vector of real numbers into a probability distribution. For regression, this activation function might be different or absent.
• Purpose: The effectiveness of the linear probe (how accurately it predicts ToM information) indicates how well and how linearly that information is encoded in the LLM's hidden states.

4.2.1.2. LatentQA for RQ1

LatentQA provides a more sophisticated approach, framing interpretability as a visual question answering problem where LLM activations are the "visual" input.

• Process (as illustrated in Figure 2, yellow path):
  1. The full dialogue $S$ is fed into a frozen target model (the LLM being analyzed).
  2. The target model produces a sequence of activation vectors $R(S) = \{ h(t_i) \mid i = 1, 2, ..., n \}$ for each token $t_i$ in the dialogue. These activations represent the LLM's internal processing of the entire dialogue.
  3. A separate decoder model is trained. This decoder receives two inputs:
     • A ToM question (e.g., "What is Agent 1's belief about the user's desire?").
     • The sequence of activation vectors R(S) from the target model.
  4. The decoder is tasked with generating the correct ToM-related labels $\hat{y}$ (the answers to the ToM question) by interpreting the activations.
• Training (as illustrated in Figure 2, orange dashed arrow): The decoder is trained using ground-truth ToM annotations as $y$. Backpropagation occurs only through the decoder model to refine its ability to extract and verbalize relevant ToM information from the target model's activations. The target model itself remains frozen during this phase.
• Purpose: LatentQA aims to extract and verbalize ToM information in a more flexible and potentially richer way than simple linear probes, allowing for open-ended questions about the LLM's internal representations.

4.2.2. RQ2: Consistency of ToM

RQ2 aims to assess the reliability and non-illusory nature of the ToM information extracted. This is crucial because a simple probe might detect ToM-like patterns that are not robust or consistent across different phrasing or contexts, leading to illusory ToM. The task is framed as a text generation problem with diverse ToM question types.

• Methods Employed:
  • Fine-tuning (FT): LLMs are fine-tuned directly on dialogue, question, and answer triplets. This adapts the model to generate correct ToM answers for specific scenarios. This approach is adapted from $Kim et al. (2023)$.
  • Chain-of-Thought (CoT) Prompting: LLMs are prompted to generate intermediate reasoning steps before providing the final ToM answer. This technique, also adapted from $Kim et al. (2023)$, encourages the model to explicitly show its reasoning process, which can improve accuracy and reveal logical inconsistencies.
  • LatentQA: The LatentQA model is applied in the same setting as RQ1, where the decoder generates answers to ToM questions based on target model activations.
• Evaluation Criterion (Key Difference): To assess consistency, a more rigorous standard is adopted, inspired by $Kim et al. (2023)$ and Chan et al. (2024). An answer is considered valid only if the decoder model produces logically consistent answers across related ToM questions. For example, if a multiple-choice question is answered correctly but a related factual question about the same ToM scenario yields a conflicting response, the overall answer is marked invalid, even if one part was technically correct. This stringent evaluation helps filter out illusory ToM.

4.2.3. RQ3: ToM-controlled Generation

For RQ3, the goal is to investigate the controllability of the LLM via its ToM components, essentially demonstrating that altering the LLM's internal ToM representations can steer its generated output.

• Process (as illustrated in Figure 2, blue and cyan paths):

  1. ToM-Alteration (Boosting):
     • The internal representation of the target model is modified from R(S) to R'(S).
     • This modification is specifically designed to target the beliefs, desires, or intentions of a character within the dialogue.
     • A hypothetical ToM question is posed, and the desired answer (reflecting the altered ToM state) is provided.
     • The gradient flow (highlighted in blue in Figure 2) is calculated by comparing the generated answer with the desired actual answer. This gradient indicates how to adjust the target model's activations to better reflect the desired ToM state.
     • This gradient flow is used to refine the decoder model (during a training-like phase) and then to boost the target representation in a way that enhances the efficiency of representing a particular ToM component (e.g., intention). The BDI paradigm (belief, desire, intention) is used to guide these specific modifications.
  2. Aligned Response Generation:
     • After the ToM-alteration (boosting) phase, only the target model (with its ToM-altered representation R'(S)) is used to produce aligned responses $C''$. This generation path is highlighted in cyan in Figure 2.
  3. Evaluation:
     • The aligned responses $C''$ are then compared to responses $C'$ generated by the unaltered model (the original LLM).
     • Both $C''$ and $C'$ are compared against ground truth human utterances $C$ from the dataset.
     • A set of advanced LLMs (referred to as judge LLMs) are employed to evaluate which response ($C''$ or $C'$) better reflects human-like behavior and alignment with the intended ToM state. The evaluation metric is the win rate (win / (win + lose)).

• Purpose: This methodology directly demonstrates the practical utility of understanding and manipulating LLM internal ToM representations for steering LLM behavior towards more aligned and human-like interaction.

4.3. Mathematical Formulas

4.3.1. Linear Probing Formula

As detailed in section 4.1, the linear probing method uses the following formula to map a hidden state to a label space: $ \hat { y } = \operatorname { s o f t m a x } ( W h ( S ) + b ) $

• $\hat{y}$: The predicted output, representing the ToM-related information (e.g., a class probability distribution for discrete labels or a continuous value for regression).
• $W$: A weight matrix that linearly transforms the hidden state h(S).
• h(S): The hidden state vector extracted from the LLM's residual stream corresponding to the final token of the input sequence $S$. This vector is an internal numerical representation of the input.
• $b$: A bias vector, added to the weighted hidden state.
• $\operatorname{softmax}(\cdot)$: The softmax activation function, which converts a vector of real numbers into a probability distribution. For a vector $z = [z_1, z_2, \ldots, z_k]$, the softmax function calculates $P(i) = \frac{e^{z_i}}{\sum_{j=1}^{k} e^{z_j}}$. This is typically used for classification tasks. For regression tasks, this function would be omitted or replaced by an appropriate activation function (e.g., identity).

4.3.2. LatentQA Interpretability Pipeline (Conceptual)

While LatentQA is described, no specific mathematical formula for its overall operation is provided in the paper's text. It's described conceptually as framing interpretability as a visual question answering problem. The process involves:

1. Encoding: A frozen target model (the LLM under study) processes the input dialogue $S$ to produce internal representations $R(S) = \{h(t_i) \mid i = 1, 2, ..., n\}$, which are sequences of activation vectors.
2. Decoding: A decoder model takes these activation vectors R(S) and a ToM question as input.
3. Generation: The decoder model then generates a natural language answer $\hat{y}$ to the ToM question, effectively verbalizing the ToM-related information implicitly present in R(S). The decoder's training involves backpropagation to minimize the difference between its generated answers and ground-truth ToM annotations $y$.

4.3.3. ToM-Controlled Generation (Conceptual)

Similar to LatentQA, ToM-controlled generation is described conceptually rather than with explicit mathematical formulas. The key idea involves modifying the target model's internal representation from R(S) to R'(S). This modification is driven by a gradient flow (illustrated in Figure 2) derived from comparing a generated answer to a hypothetical ToM question with a desired actual answer. This gradient is then used to boost or steer the target representation towards encoding specific ToM components (beliefs, desires, intentions). After this steering, the target model generates responses $C''$ that reflect the ToM-altered state.

4.3.4. Win Rate Metric for Controllability

The win rate is defined as: $ \text{Win Rate} = \frac{\text{win}}{\text{win} + \text{lose}} $

• $\text{win}$: The number of times the ToM-aligned model's response ($C''$) is judged by LLM referees to be better or more aligned with human-like behavior than the out-of-the-box (unaligned) model's response ($C'$).
• $\text{lose}$: The number of times the ToM-aligned model's response ($C''$) is judged to be worse than the unaligned model's response ($C'$). (Implicitly, draws might be excluded or counted in a way that doesn't increase either win or lose counts for this specific formula).

5. Experimental Setup

5.1. Datasets

The research utilizes several datasets, categorized by their suitability for investigating ToM properties and alignment.

• For investigating ToM (RQ1 - pragmatic aspects of language):

  • CaSiNo Dataset (Chawla et al., 2021):
    • Source: Human-written conversations.
    • Characteristics: Consists of dialogues where two agents negotiate how to divide items for a picnic (food, water, firewood).
    • Domain: Negotiation, multi-party dialogue.
    • Purpose: Chosen to ensure a variety of linguistic variations and reliable pragmatic markers, as ToM is deeply intertwined with language pragmatics. It implicitly or explicitly reveals agent priorities.
    • Data Sample Example (Conceptual): A dialogue snippet might involve Agent 1 saying, "I really need the water because I'm going hiking," and Agent 2 responding, "I'm concerned about starting a fire, so firewood is my priority." The goal is to infer their priorities.
  • CRAIGSLISTBARGAIN Dataset (He et al., 2018):
    • Source: Real-world online bargaining dialogues.
    • Characteristics: Focuses on negotiations over secondhand items (e.g., cars, bicycles) on the Craigslist platform.
    • Domain: Negotiation, bargaining, economic interaction.
    • Purpose: Similar to CaSiNo, it captures real-world pragmatic language use where intentions and values (like price expectations) are often implied rather than stated directly. The task is to predict the price each party has in mind.
    • Data Sample Example (Conceptual): A conversation where a seller might say, "It's in great condition, barely used," and a buyer counters with, "I've seen similar items for less." The goal is to infer their internal target prices.

• For verifying non-illusory ToM (RQ2 - consistency) and enhancing alignment (RQ3):

  • FanToM Dataset (Kim et al., 2023):
    • Source: Designed to stress-test Machine Theory of Mind.
    • Characteristics: Rich in annotations and provides greater controllability over experiments due to its structured nature.
    • Domain: ToM assessment, social interaction.
    • Purpose: Specifically designed to assess the consistency of ToM by posing diverse ToM question types.
    • Data Sample Example (Conceptual): A short story about a character with a false belief, followed by multiple questions about that character's belief, desire, and intention from different perspectives, designed to check for logical consistency.
  • NegotiationToM Dataset (Chan et al., 2024):
    • Source: Designed to assess ToM in negotiation scenarios.
    • Characteristics: Provides granularity of annotations at the utterance level, including beliefs, desires, and intentions for each participant in a dialogue.
    • Domain: Negotiation, ToM assessment, social interaction.
    • Purpose: Highly suitable for RQ3 (enhancing alignment) due to its rich utterance-level annotation schema, which allows for precise control over altering individual ToM components and measuring corresponding output changes.
    • Data Sample Example (Conceptual): A dialogue where an agent says, "I really need water because I'm feeling thirsty," and the utterance is annotated with Agent 1's desire: water (high priority).

Dataset Splitting:

• For CaSiNo and CRAIGSLISTBARGAIN, the methodology adheres to their original train, validation, and test splits.
• For FanToM and NegotiationToM, which lacked pre-defined splits, the authors generated reproducible splits as detailed in Appendix A: a 30% test set and the remaining 70% split into 80:20 for training and validation.

5.2. Evaluation Metrics

For every evaluation metric mentioned in the paper, a complete explanation is provided below.

5.2.1. Exact Match Accuracy

• Conceptual Definition: Exact Match Accuracy is a straightforward metric used to evaluate classification tasks or where the model's output must precisely match a reference answer. It quantifies the proportion of predictions that are identical to the ground truth. It is used in RQ1 for the CaSiNo dataset and for belief, desire, and intention predictions in NegotiationToM for RQ2. For consistency, a more stringent version may require all related questions to be exact matches.
• Mathematical Formula: $ \text{Accuracy} = \frac{\text{Number of correct predictions}}{\text{Total number of predictions}} $
• Symbol Explanation:
  • Number of correct predictions: The count of instances where the model's predicted output is identical to the ground truth label.
  • Total number of predictions: The total number of instances evaluated.

5.2.2. $R^2$ Score (Coefficient of Determination)

• Conceptual Definition: The $R^2$ score is a performance metric used in regression analysis. It measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). In simpler terms, it indicates how well the observed data are replicated by the model, based on the proportion of total variation of outcomes explained by the model. A higher $R^2$ (closer to 1) indicates a better fit. It is used in RQ1 for the CRAIGSLISTBARGAIN dataset to assess price prediction.
• Mathematical Formula: $ R^2 = 1 - \frac{\sum_{i=1}^{N} (y_i - \hat{y}i)^2}{\sum{i=1}^{N} (y_i - \bar{y})^2} $
• Symbol Explanation:
  • $N$: The total number of observations (data points).
  • $y_i$: The actual (ground truth) value of the dependent variable for the $i$-th observation.
  • $\hat{y}_i$: The predicted value of the dependent variable for the $i$-th observation, as output by the model.
  • $\bar{y}$: The mean of the actual values of the dependent variable, calculated as $\bar{y} = \frac{1}{N} \sum_{i=1}^{N} y_i$.
  • $\sum_{i=1}^{N} (y_i - \hat{y}_i)^2$: This is the sum of squared residuals (or residual sum of squares), representing the unexplained variance by the model.
  • $\sum_{i=1}^{N} (y_i - \bar{y})^2$: This is the total sum of squares, representing the total variance in the dependent variable.

5.2.3. ALL* Score (FanToM)

• Conceptual Definition: The ALL* score is a highly stringent consistency metric specifically used for the FanToM dataset. It requires the model to correctly answer all six types of ToM questions (e.g., belief, desire, intention, false belief, etc.) for the same piece of information within a conversation. If even one of the six related questions is answered incorrectly or inconsistently, the entire set is considered invalid. This metric is designed to assess a model's holistic and robust understanding across various ToM aspects.
• Mathematical Formula: The paper does not provide a specific formula for $ALL*$, but based on its definition, it can be understood as: $ \text{ALL}^* = \frac{\text{Number of instances where all 6 ToM questions are correct}}{\text{Total number of instances}} $
• Symbol Explanation:
  • Number of instances where all 6 ToM questions are correct: The count of ToM scenarios where the model provided the correct answer for every one of the six specific ToM question types associated with that scenario.
  • Total number of instances: The total number of ToM scenarios evaluated.

5.2.4. ALL Score (FanToM & NegotiationToM)

• Conceptual Definition: The ALL score is a general consistency metric that aggregates the correctness of multiple ToM components. For NegotiationToM, it measures cases where the model correctly predicts all three components (belief, desire, and intention) for a given utterance. For FanToM, it refers to a less stringent aggregation than $ALL*$, often implying correct answers to a predefined subset of ToM questions for a scenario (details may vary by original dataset's definition).
• Mathematical Formula: The paper does not provide a specific formula for ALL, but based on its definition for NegotiationToM, it can be understood as: $ \text{ALL} = \frac{\text{Number of instances where all BDI components are correct}}{\text{Total number of instances}} $
• Symbol Explanation:
  • Number of instances where all BDI components are correct: The count of utterances (for NegotiationToM) or scenarios (for FanToM) where the model's predictions for belief, desire, and intention (or other relevant ToM aspects) all perfectly match the ground truth.
  • Total number of instances: The total number of utterances or scenarios evaluated.

5.2.5. Micro F1 Score (for Intention)

• Conceptual Definition: The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both. It is particularly useful when dealing with imbalanced classes. Micro F1 calculates F1 globally by counting the total true positives, false negatives, and false positives. This means it aggregates contributions from all classes to compute the average F1. It is used for intention predictions in NegotiationToM, as intentions can be multi-label and potentially imbalanced.
• Mathematical Formula: First, define Precision and Recall: $ \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}} $ $ \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} $ Then, the Micro F1 Score is: $ \text{Micro F1} = 2 \times \frac{\text{Micro Precision} \times \text{Micro Recall}}{\text{Micro Precision} + \text{Micro Recall}} $ Where:
  • Micro Precision = $\frac{\sum_{c=1}^{C} \text{TP}_c}{\sum_{c=1}^{C} (\text{TP}_c + \text{FP}_c)}$
  • Micro Recall = $\frac{\sum_{c=1}^{C} \text{TP}_c}{\sum_{c=1}^{C} (\text{TP}_c + \text{FN}_c)}$
• Symbol Explanation:
  • True Positives (TP): Instances correctly predicted as positive.
  • False Positives (FP): Instances incorrectly predicted as positive (Type I error).
  • False Negatives (FN): Instances incorrectly predicted as negative (Type II error).
  • $C$: The total number of classes (distinct intention labels).
  • $\text{TP}_c$, $\text{FP}_c$, $\text{FN}_c$: True Positives, False Positives, False Negatives for a specific class $c$.
  • Micro Precision: Precision calculated by summing up the TP, FP across all classes, and then applying the precision formula.
  • Micro Recall: Recall calculated by summing up the TP, FN across all classes, and then applying the recall formula.

5.2.6. Win Rate (for ToM-controlled Generation)

• Conceptual Definition: The win rate is a comparative metric used in RQ3 to assess the effectiveness of ToM-informed LLM responses against baseline (unaligned) responses, as judged by LLM referees. It quantifies the proportion of times the ToM-aligned model's output is deemed superior.
• Mathematical Formula: $ \text{Win Rate} = \frac{\text{Number of Wins}}{\text{Number of Wins} + \text{Number of Losses}} $
• Symbol Explanation:
  • Number of Wins: The count of instances where the ToM-informed model's response ($C''$) was judged to be better than the out-of-the-box (unaligned) model's response ($C'$).
  • Number of Losses: The count of instances where the ToM-informed model's response ($C''$) was judged to be worse than the out-of-the-box (unaligned) model's response ($C'$). (Cases where the judge LLM declares a tie or "neither" are typically excluded from both win and loss counts in this ratio, or handled separately, as implied by the formula win / (win + lose)).

5.3. Baselines

The paper compares its proposed methods against various baselines depending on the research question:

• For RQ1 (Reading ToM):
  • Linear Probing: This is used as a baseline for LatentQA. Linear probing is a simpler, more direct method to assess information encoding in LLM internal representations, making it a natural comparison point for LatentQA's more complex approach.
• For RQ2 (Consistency of ToM):
  • Fine-tuning (FT): A common approach where LLMs are trained on task-specific data. This serves as a strong supervised learning baseline for ToM consistency.
  • Chain-of-Thought (CoT) Prompting: A popular technique to elicit reasoning from LLMs without explicit training. This is used to gauge LLM performance in ToM reasoning purely through prompting.
  • GPT-4o-mini (for CoT): A widely recognized OpenAI model, used as an external baseline to provide an intuitive comparison for the CoT approach, helping to benchmark the studied LLaMA models against a state-of-the-art proprietary LLM.
• For RQ3 (ToM-controlled Generation):

  • Out-of-the-Box (Unaligned) Model: The performance of the ToM-informed target model ($C''$) is directly compared against the responses generated by the same LLaMA model without any ToM-informed steering ($C'$). This establishes whether the ToM-driven alignment provides a measurable improvement.

    Judge LLMs: For RQ3, the evaluation itself relies on a set of advanced LLMs acting as judges. These referee models are:

• An ol reasoning model (specific type not detailed, but described as a reasoning model).
• GPT-4o (by OpenAI).
• Gemini 1.5 Pro (by Google). These highly capable LLMs are chosen to provide robust and nuanced evaluations of response quality and ToM alignment, given the difficulty of human-like judgment for such tasks at scale. Simple shuffling of candidate responses is employed to avoid positional bias.

5.4. Experimental Settings

The experimental settings are comprehensive, reflecting the variety of experiments conducted.

• Model Variants:
  • LLaMA 3 variants: 1B, 3B, and 8B parameter models.
  • GPT-4o-mini (for CoT baseline), GPT-4o, and Gemini 1.5 Pro (as judge LLMs).
• Layer Depth Analysis (for RQ1):
  • Experiments for Reading ToM (RQ1) are conducted at three distinct depth levels of internal representations (R(S)): shallow, middle, and deep layers, to analyze how layer depth influences ToM accuracy.
  • Specific layer mappings:
    • LLaMA 3B and 8B: Layers 5 (shallow), 15 (middle), and 25 (deep).
    • LLaMA 1B: Layers 3 (shallow), 8 (middle), and 14 (deep).
• Consistency Experiments (for RQ2):
  • For ToM consistency (RQ2), the depth of R(S) is fixed to the middle layer (= 15) to minimize reliance on syntactic information while retaining sufficient semantic ToM-related information.
• Hyperparameters:
  • LatentQA: Hyperparameters are adopted directly from their official implementations.
  • Fine-tuning (Appendix I): max_seq_length (2000/2500 for NegotiationToM/FanToM), per_device_train_batch_size (32/8), gradient_accumulation_steps (4/2), warmup_steps (5), epochs (5), learning_rate (5e-4), optim ("adamw_8bit"), weight_decay (0.01), lr_scheduler_type ("linear"), seed (3407), target_modules (["q_proj", "k_proj"]). Both full-precision and 4-bit variants are used for efficiency and performance comparison.
  • Linear Probing (Appendix J): Logistic Regression (for classification) and Ridge Regression (for regression) from scikit-learn. PCA is used for dimensionality reduction. Hyperparameters for grid search include classifier__C, classifier__penalty, classifier__solver for Logistic Regression, and pca__n_components for Ridge Regression.
  • ToM Steering (Appendix H): 300 steering samples, target representations from the middle layer (= 15). Key hyperparameters: module_setup ("read-vary_write -fixed_n-fixed"), modify_chat_template (True), shift_position_ids (True), lr (5e-5), seed (42), batch_size_training (1), samples (50), layers_to_optimize (0 to 15), per_layer_loss (False), qa_per_layer (False).
• Prompt Templates:
  • Judge LLMs (Appendix B): Specific prompts are designed to prioritize grammatical coherence and ToM alignment, while avoiding direct references to context-specific terms. A separate, more concise prompt is used for the ol reasoning model.
  • LatentQA Q&A templates (Appendix C): Templates are provided for each dataset (CaSiNo, CRAIGSLISTBARGAIN, NegotiationToM, FanToM) to structure how ToM questions and answers are generated for LatentQA training.
  • ToM Steering question templates (Appendix D): Templates for Belief, Desire, and Intention questions are used to facilitate targeted ToM manipulation.
  • CoT templates (Appendix E): A 7-shot template for CoT reasoning is provided for ToM consistency experiments.
• Sample Selection for Controllability (Appendix G): For RQ3, samples are carefully selected from the NegotiationToM dataset to mimic natural dialogue flow, ensuring that utterances for Belief, Desire, and Intention manipulation immediately follow relevant updates in the conversation.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results provide insights into the LLMs' ability to capture, consistently reason about, and be controlled by Theory of Mind (ToM) information.

6.1.1. Extraction of ToM-related Information (RQ1)

Based on Table 1, three main observations are made:

• LatentQA vs. Linear Probing: LatentQA consistently outperforms linear probing. This suggests that ToM-related information, while present in single activation components, is not sufficiently captured by simple linear mappings. LatentQA's ability to interpret a sequence of activations (rather than just a single point) allows it to reconstruct underlying mental states more effectively.
• Optimal Layer Depth: The highest performance for LatMQA in reading ToM is frequently observed at intermediate layers (five out of six experiments).
  • Shallower layers may lack semantic richness.
  • Deeper layers might be less optimal for ToM due to self-centric pretraining data (Sap et al., 2022), making it harder to generalize to diverse narrative structures involving others' mental states.
  • Conversely, linear probing shows higher performance in deeper layers, which could imply ToM information is encoded differently or becomes more linearly separable at later stages when filtered for simpler probes.
• Prediction Orthogonality Hypothesis: The model's success in predicting numerical values like seller or buyer prices in the CRAIGSLISTBARGAIN dataset, despite LLMs not being explicitly designed for regression tasks or this not aligning with pretraining objectives, is interpreted under the Prediction Orthogonality Hypothesis (Bereska and Gavves, 2024). This hypothesis suggests that LLMs might learn to represent information (like prices) orthogonally to their primary predictive task, making it accessible even if not directly trained for it.
• Model Size Impact: ToM-related information is represented in the inner layers of an LLM, and its effectiveness appears directly related to the size of the model. Larger models tend to capture more or more accessible ToM information.

6.1.2. Consistency of ToM (RQ2)

• Inconsistency in State-of-the-Art LLMs: As shown in Table 2, Table 3, and Table 4, state-of-the-art LLMs still do not exhibit a consistent understanding of ToM. This is particularly evident in the low $ALL*$ and ALL scores, which require coherent answers across multiple ToM questions.
• LatentQA's Promising Role: Despite overall inconsistency, LatentQA presents promising opportunities compared to CoT reasoning and fine-tuning. It offers better performance than CoT and is comparable with fine-tuning, while requiring less computational power and providing finer granularity for controllability.
• Performance Gaps: The performance gap between LatentQA and the fine-tuned model is more pronounced for individual ToM components but becomes more uniform for combined metrics as LLM size increases. This suggests that while fine-tuning might be better at individual components, LatentQA scales better for holistic ToM representation, especially with larger LLMs.
• Dataset Ambiguity: The observed inconsistency is partly attributed to the inherent ambiguity of the datasets themselves. Even human performance on ToM benchmarks (e.g., 87.5% for FANTOM and 43.78% for NegotiationToM) is not perfect, setting a realistic upper bound for LLM performance. The findings indicate that even imperfect ToM information, when extracted and enhanced, can achieve moderately practical levels of accuracy for alignment.

6.1.3. Using ToM for Controllability (RQ3)

The results from the controllability experiments (Figure 3) indicate that leveraging ToM for steering LLMs towards generating aligned responses is a promising direction.

• Win Rates: The weighted average win rates are 67.15% for the 3B model and 63.25% for the 8B model. This demonstrates that ToM-informed steering can make LLM responses more human-like and aligned.
• Lowest Performing Intentions: ShowEmpathy and Describe-Need had the lowest performance in terms of improvement from ToM-alignment. This is hypothesized to be because LLMs can naturally express these intentions due to their prevalence in pretraining data, leaving less room for ToM-driven alignment to make a significant difference.
• Model Size and Improvement: The 3B model often improves more than the 8B model after alignment, despite the 8B model having lower accuracy in reading ToM. This is attributed to the greater inherent capability of the unaligned 8B model, meaning it starts from a higher baseline, and the steering has less impact on its already competent responses.
• Fine-grained Controllability: The findings are significant from a theoretical perspective, suggesting that ToM representations can be steered in a fine-grained manner at various levels. This includes manipulating first-order ToM (an agent's beliefs about the world) and second-order ToM (an agent's beliefs about another agent's beliefs), demonstrated by targeting the belief component through complex linguistic constructions.

6.2. Data Presentation (Tables)

The following are the results from the tables of the original paper:

The following are the results from Table 1 of the original paper:

The following are the results from Table 2 of the original paper:

The following are the results from Table 3 of the original paper:

The following are the results from Table 4 of the original paper:

6.3. Ablation Studies / Parameter Analysis

While the paper does not explicitly present sections labeled "ablation studies," several experimental comparisons serve a similar purpose by isolating and evaluating the impact of different methodological choices and model components.

• Comparison of LatentQA vs. Linear Probing (RQ1): This comparison (Table 1) acts as an ablation study for the ToM reading methodology. It demonstrates that the more sophisticated LatentQA approach, which leverages LLM activations as context for a decoder model, is significantly more effective at extracting ToM-related information than a simple linear probe. This validates the necessity of a more expressive interpretability method for ToM.

• Analysis of Layer Depth (RQ1): The experiments across shallow, middle, and deep layers (Table 1) analyze the parameter of LLM depth. The finding that LatentQA performs best at intermediate layers for ToM (while linear probing prefers deeper layers) is a crucial insight into where ToM information is optimally represented within the model's architecture. This parameter analysis guides the choice of middle layer for subsequent ToM consistency and controllability experiments.

• Comparison of LatentQA, Fine-tuning (FT), and CoT (RQ2): This multi-method comparison (Table 2, Table 3, Table 4) effectively acts as an ablation study for ToM consistency. It evaluates which method most reliably captures and verbalizes ToM information under stringent consistency metrics. LatentQA's competitive performance against FT and superiority over CoT (especially considering efficiency) highlights its value as a practical approach for ToM tasks.

• Impact of Model Size: Across all experiments, the performance of LLaMA 3B and LLaMA 3 8B models is consistently compared. This parameter analysis shows that ToM representation effectiveness is generally related to model size (Table 1), and also reveals nuanced behaviors, such as the 3B model sometimes showing greater improvement from ToM-alignment than the 8B model (Figure 3), which is attributed to the 8B's higher baseline capability.

• Targeting Specific BDI Components (RQ3): The controllability experiments (Figure 3) effectively ablate the specific ToM components (Belief, Desire, Intention) targeted for steering. By separately manipulating these, the study demonstrates that ToM-driven alignment is effective, and also identifies which intention labels (ShowEmpathy, Describe-Need) show less improvement, suggesting areas where inherent model capabilities already meet or exceed the steered behavior.

  These comparisons and analyses of different experimental settings and model configurations serve to dissect the effectiveness of various components and parameters within the proposed ToM-driven alignment framework.

7. Conclusion & Reflections

7.1. Conclusion Summary

This research successfully explored the methods for extracting Theory of Mind (ToM)-related information from the internal layers of Large Language Models (LLMs). It rigorously examined how model depth, size, and various interpretability techniques (like Linear Probing and LatentQA) influence the capture of ToM. The study also quantified the extent to which this information contributes to forming a consistent ToM representation within LLMs. Crucially, the paper introduced and validated an innovative approach for utilizing this ToM information to steer LLMs toward generating more aligned and human-like responses in conversational settings. The findings demonstrate that ToM-informed alignment significantly improves LLM response quality, achieving notable win rates for LLaMA 3 models. This work positions ToM-driven strategies as a promising avenue for enhancing alignment in LLM-based conversational agents.

7.2. Limitations & Future Work

The authors acknowledge several limitations and suggest future research directions:

7.2.1. Methodological Limitations

• Reliance on LLMs for Evaluation: The primary limitation is the reliance on LLMs (GPT-4o, Gemini 1.5 Pro) as judges for evaluating response quality and human-like behavior. While convenient, the authors suggest that human judges would likely yield more reliable assessments, given the close relationship between pragmatic aspects of language and ToM. This introduces a potential circularity in evaluation if the judge LLMs themselves have ToM limitations.

7.2.2. Technical Limitations and Challenges

• Controllability Datasets: Controllability experiments were limited to a single dataset (NegotiationToM) due to a scarcity of suitable alternatives that possess both ToM-related labels and utterance-level annotations. This restricts the generalizability of the controllability findings.
• Hyperparameter Sensitivity: Generating aligned responses through ToM steering proved unstable and highly dependent on hyperparameter tuning. This non-trivial aspect can affect reproducibility and practical implementation.
• Model Selection: The study focused exclusively on a single family of LLMs (LLaMA 3 variants) and models with fewer than 8 billion parameters. This limits the generalizability of findings across diverse LLM architectures and larger models.
• Baselines Inconsistencies:
  • For ToM reading, a 4-bit precision version of the 8B model (from Unsloth) was used for better results compared to the full-precision model, introducing a slight inconsistency in model comparison.
  • For ToM consistency experiments, their implementation of CoT fell short of expectations, specifically underperforming a 3B Flan-T5-XL model reported in the FanToM reference article, which was not publicly available. This raises questions about the direct comparability of their CoT baseline.

7.2.3. Practical Limitations

• Manual Q&A Design for Steering: The process of designing question-answer pairs for model steering currently requires human experts. For real-world LLM-based agents, this process needs to be automated dynamically, potentially using LLMs themselves as planners to execute the steering pipeline.

7.2.4. Ethical Considerations

• Privacy, Autonomy, and Manipulation: The increasing ability of LLMs to infer and simulate mental states raises serious ethical concerns. Tailoring responses based on users' presumed mental states without their awareness or consent could lead to exploitation, manipulation, or privacy breaches in commercial, political, or interpersonal contexts. The authors emphasize the need for rigorous oversight, transparent design, and adherence to informed consent and value alignment principles.

7.2.5. Future Work

• Integration into Real-world Agents: Integrating ToM techniques into real-world LLM-based agents to test their practical applicability.
• Refining Evaluation Methodologies: Improving evaluation methodologies, potentially incorporating more sophisticated human-based evaluations.
• Expanding Scope: Including a wider variety of language models to verify the consistency and robustness of the findings.

7.3. Personal Insights & Critique

This paper presents a significant step forward in making LLMs more aligned and human-like by actively leveraging Theory of Mind. The distinction between merely assessing ToM and exploiting it for controllability is a crucial conceptual leap. The use of LatentQA for fine-grained manipulation of beliefs, desires, and intentions is particularly innovative, moving beyond brute-force fine-tuning towards a more interpretable and targeted approach to alignment. The framework demonstrates that even with imperfect ToM capabilities, strategic intervention can yield practical improvements in LLM behavior.

Inspirations and Applications:

• The methodology of probing and then steering internal representations could be transferred to other domains beyond ToM. For instance, LLMs could be steered to adhere to specific ethical guidelines, legal frameworks, or even stylistic preferences by identifying and manipulating relevant internal features.
• The BDI framework provides a robust psychological foundation, making the ToM-driven alignment more structured and interpretable compared to black-box reinforcement learning from human feedback (RLHF). This could lead to more robust and explainable alignment processes.
• The concept of ToM-informed LLMs has profound implications for human-AI collaboration, potentially enabling AI to anticipate user needs, adapt its communication style, and foster greater trust and efficiency in interactions, particularly in fields requiring high social intelligence like education, healthcare, and customer service.

Potential Issues, Unverified Assumptions, or Areas for Improvement:

• The "LLM as a Judge" Problem: The methodological limitation of using LLMs to evaluate human-like behavior is a critical concern. While practical for scale, it creates a potential circular dependency: LLMs are being trained and evaluated by other LLMs on what constitutes "human-like." The very ToM capabilities being assessed in the target model might also influence the judge model's assessment, leading to a potentially illusory alignment rather than true human-like alignment. Future work absolutely needs human evaluation to validate these claims.

• Reproducibility of Baselines: The inability to perfectly reproduce the FanToM baseline results (where the authors' CoT implementation underperformed a reference Flan-T5 model) hints at potential fragility or undisclosed nuances in ToM evaluation, underscoring the complexity of the field.

• Hyperparameter Sensitivity: The noted hyperparameter sensitivity for steering suggests that practical deployment might be challenging without robust auto-tuning mechanisms. The elegance of the ToM-driven alignment could be undermined by the practical difficulty of finding stable parameters.

• Scalability to Larger Models: The study focuses on LLaMA 3 up to 8B parameters. While the 8B model showed some ToM capabilities, it's unclear how LatentQA and ToM steering would scale to much larger, more capable models (e.g., 70B or open-source models approaching GPT-4 level) or whether the observed ToM "inconsistencies" would diminish or change character.

• Specificity of ToM: The paper effectively manipulates high-level BDI components. However, ToM is highly nuanced. It would be insightful to explore if such fine-grained steering can influence subtle aspects like sarcasm, white lies, or nuanced social cues, which are hallmarks of advanced human ToM.

  Overall, this paper provides a valuable contribution by demonstrating a viable path towards actively shaping LLM behavior through ToM representations, opening new research avenues in controllable and socially intelligent AI.