# HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models

arXiv: https://arxiv.org/abs/2310.16755 · HTML: https://mlsanity.com/paper/2310.16755/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Data Contamination: From Memorization to Exploitation](https://mlsanity.com/paper/2203.08242/) (2022)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Sources of Hallucination by Large Language Models on Inference Tasks](https://mlsanity.com/paper/2305.14552/) (2023)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers](https://mlsanity.com/paper/2103.16553/) (2021)
- [Evaluating Theory of Mind in Question Answering](https://mlsanity.com/paper/1808.09352/) (2018)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [Grounded Language Learning Fast and Slow](https://mlsanity.com/paper/2009.01719/) (2020)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [Emergence of Maps in the Memories of Blind Navigation Agents](https://mlsanity.com/paper/2301.13261/) (2023)
- [Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering](https://mlsanity.com/paper/2304.13911/) (2023)

## Built on by (most cited first, 30 of 63)

- [LLMs achieve adult human performance on higher-order theory of mind tasks](https://mlsanity.com/paper/2405.18870/) (2024)
- [Embodied AI Agents: Modeling the World](https://mlsanity.com/paper/2506.22355/) (2025)
- [SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs](https://mlsanity.com/paper/2410.13648/) (2024)
- [Language Models use Lookbacks to Track Beliefs](https://mlsanity.com/paper/2505.14685/) (2025)
- [SPRIG: Improving Large Language Model Performance by System Prompt Optimization](https://mlsanity.com/paper/2410.14826/) (2024)
- [LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation](https://mlsanity.com/paper/2501.05414/) (2025)
- [Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models](https://mlsanity.com/paper/2502.11881/) (2025)
- [AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling](https://mlsanity.com/paper/2502.15676/) (2025)
- [Re-evaluating Theory of Mind evaluation in large language models](https://mlsanity.com/paper/2502.21098/) (2025)
- [EmoAgent: Assessing and Safeguarding Human-AI Interaction for Mental Health Safety](https://mlsanity.com/paper/2504.09689/) (2025)
- [Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models](https://mlsanity.com/paper/2505.02847/) (2025)
- [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026)
- [LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models](https://mlsanity.com/paper/2408.15778/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions](https://mlsanity.com/paper/2506.23046/) (2025)
- [Social World Models](https://mlsanity.com/paper/2509.00559/) (2025)
- [Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection](https://mlsanity.com/paper/2501.15355/) (2025)
- [OdysSim: Building Foundation Models for Human Behavior Simulation](https://mlsanity.com/paper/2606.14199/) (2026)
- [ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind](https://mlsanity.com/paper/2505.22961/) (2025)
- [Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind](https://mlsanity.com/paper/2502.21297/) (2025)
- [Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?](https://mlsanity.com/paper/2504.01698/) (2025)
- [LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game](https://mlsanity.com/paper/2510.04980/) (2025)
- [AI Awareness](https://mlsanity.com/paper/2504.20084/) (2025)
- [AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses](https://mlsanity.com/paper/2608.12307/) (2026)
- [Belief in the Machine: Investigating Epistemological Blind Spots of Language Models](https://mlsanity.com/paper/2410.21195/) (2024)
- [R^3-VQA: "Read the Room" by Video Social Reasoning](https://mlsanity.com/paper/2505.04147/) (2025)
- [MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents](https://mlsanity.com/paper/2511.23055/) (2025)
- [Social-R1: Towards Human-like Social Reasoning in LLMs](https://mlsanity.com/paper/2603.09249/) (2026)
- [The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind](https://mlsanity.com/paper/2506.20664/) (2025)
- [Investigating VLM Hallucination from a Cognitive Psychology Perspective: A First Step Toward Interpretation with Intriguing Observations](https://mlsanity.com/paper/2507.03123/) (2025)

## Abstract (arXiv)

Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. ToM plays a critical role in the development of intelligence, language understanding, and cognitive processes. While previous work has primarily focused on first and second-order ToM, we explore higher-order ToM, which involves recursive reasoning on others' beliefs. We introduce HI-TOM, a Higher Order Theory of Mind benchmark. Our experimental evaluation using various Large Language Models (LLMs) indicates a decline in performance on higher-order ToM tasks, demonstrating the limitations of current LLMs. We conduct a thorough analysis of different failure cases of LLMs, and share our thoughts on the implications of our findings on the future of NLP.