# ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind

arXiv: https://arxiv.org/abs/2305.15068 · HTML: https://mlsanity.com/paper/2305.15068/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Evaluating Theory of Mind in Question Answering](https://mlsanity.com/paper/1808.09352/) (2018)
- [True Few-Shot Learning with Prompts -- A Real-World Perspective](https://mlsanity.com/paper/2111.13440/) (2021)
- [COKE: A Cognitive Knowledge Graph for Machine Theory of Mind](https://mlsanity.com/paper/2305.05390/) (2023)
- [Mind the gap: Challenges of deep learning approaches to Theory of Mind](https://mlsanity.com/paper/2203.16540/) (2022)
- [MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic](https://mlsanity.com/paper/2305.03353/) (2023)

## Built on by (most cited first, 28 of 28)

- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://mlsanity.com/paper/2402.15052/) (2024)
- [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2402.06044/) (2024)
- [EmoBench: Evaluating the Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2402.12071/) (2024)
- [Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests](https://mlsanity.com/paper/2310.20320/) (2023)
- [Language Models Represent Beliefs of Self and Others](https://mlsanity.com/paper/2402.18496/) (2024)
- [Theory of Mind in Large Language Models: Assessment and Enhancement](https://mlsanity.com/paper/2505.00026/) (2025)
- [Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models](https://mlsanity.com/paper/2407.06004/) (2024)
- [PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models](https://mlsanity.com/paper/2403.02246/) (2024)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters](https://mlsanity.com/paper/2501.01705/) (2025)
- [Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models](https://mlsanity.com/paper/2307.03762/) (2023)
- [Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities](https://mlsanity.com/paper/2405.11841/) (2024)
- [Belief in the Machine: Investigating Epistemological Blind Spots of Language Models](https://mlsanity.com/paper/2410.21195/) (2024)
- [EnigmaToM: Improve LLMs' Theory-of-Mind Reasoning Capabilities with Neural Knowledge Base of Entity States](https://mlsanity.com/paper/2503.03340/) (2025)
- [Does Conceptual Representation Require Embodiment? Insights From Large Language Models](https://mlsanity.com/paper/2305.19103/) (2023)
- [Multi-ToM: Evaluating Multilingual Theory of Mind Capabilities in Large Language Models](https://mlsanity.com/paper/2411.15999/) (2024)
- [ToM-SSI: Evaluating Theory of Mind in Situated Social Interactions](https://mlsanity.com/paper/2509.05066/) (2025)
- [CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models](https://mlsanity.com/paper/2601.15628/) (2026)
- [Towards properly implementing Theory of Mind in AI systems: An account of four misconceptions](https://mlsanity.com/paper/2503.16468/) (2025)
- [XToM: Exploring the Multilingual Theory of Mind for Large Language Models](https://mlsanity.com/paper/2506.02461/) (2025)
- [Evaluating Theory of (an uncertain) Mind: Predicting the Uncertain Beliefs of Others in Conversation Forecasting](https://mlsanity.com/paper/2409.14986/) (2024)
- [Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations](https://mlsanity.com/paper/2605.15205/) (2026)
- [A formal definition and meta-model for a machine theory of mind](https://mlsanity.com/paper/2606.03471/) (2026)
- [Spot The Ball: A Benchmark for Visual Social Inference](https://mlsanity.com/paper/2511.00261/) (2025)
- [CoMMET: A Psychologically Grounded Benchmark for Evaluating Theory of Mind in Multimodal LLMs](https://mlsanity.com/paper/2603.11915/) (2026)
- [CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models](https://mlsanity.com/paper/2604.10031/) (2026)
- [OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind](https://mlsanity.com/paper/2605.20423/) (2026)

## Abstract (arXiv)

Theory of Mind (ToM), the capacity to comprehend the mental states of distinct individuals, is essential for numerous practical applications. With the development of large language models (LLMs), there is a heated debate about whether they are able to perform ToM tasks. Previous studies have used different tasks and prompts to test the ToM on LLMs and the results are inconsistent: some studies asserted these models are capable of exhibiting ToM, while others suggest the opposite. In this study, We present ToMChallenges, a dataset for comprehensively evaluating the Theory of Mind based on the Sally-Anne and Smarties tests with a diverse set of tasks. In addition, we also propose an auto-grader to streamline the answer evaluation process. We tested three models: davinci, turbo, and gpt-4. Our evaluation results and error analyses show that LLMs have inconsistent behaviors across prompts and tasks. Performing the ToM tasks robustly remains a challenge for the LLMs. In addition, our paper wants to raise awareness in evaluating the ToM in LLMs and we want to invite more discussion on how to design the prompts and tasks for ToM tasks that can better assess the LLMs' ability.