# Does ChatGPT have Theory of Mind?

arXiv: https://arxiv.org/abs/2305.14020 · HTML: https://mlsanity.com/paper/2305.14020/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 13 of 13)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Probing Neural Network Comprehension of Natural Language Arguments](https://mlsanity.com/paper/1907.07355/) (2019)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Probing Across Time: What Does RoBERTa Know and When?](https://mlsanity.com/paper/2104.07885/) (2021)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?](https://mlsanity.com/paper/2302.03495/) (2023)

## Built on by (most cited first, 16 of 16)

- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://mlsanity.com/paper/2308.12488/) (2023)
- [Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models](https://mlsanity.com/paper/2310.19619/) (2023)
- [(Ir)rationality and Cognitive Biases in Large Language Models](https://mlsanity.com/paper/2402.09193/) (2024)
- [Re-evaluating Theory of Mind evaluation in large language models](https://mlsanity.com/paper/2502.21098/) (2025)
- [Recent Advancement of Emotion Cognition in Large Language Models](https://mlsanity.com/paper/2409.13354/) (2024)
- [LLM Cognitive Judgements Differ From Human](https://mlsanity.com/paper/2307.11787/) (2023)
- [The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?](https://mlsanity.com/paper/2308.14242/) (2023)
- [Improving Language Models for Emotion Analysis: Insights from Cognitive Science](https://mlsanity.com/paper/2406.10265/) (2024)
- [Exploring Human-LLM Conversations: Mental Models and the Originator of Toxicity](https://mlsanity.com/paper/2407.05977/) (2024)
- [THiNK: Can Large Language Models Think-aloud?](https://mlsanity.com/paper/2505.20184/) (2025)
- [Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4](https://mlsanity.com/paper/2306.07622/) (2023)
- [A criterion for Artificial General Intelligence: hypothetic-deductive reasoning, tested on ChatGPT](https://mlsanity.com/paper/2308.02950/) (2023)
- [Can AI and humans genuinely communicate?](https://mlsanity.com/paper/2402.09494/) (2024)
- [Causes in neuron diagrams, and testing causal reasoning in Large Language Models. A glimpse of the future of philosophy?](https://mlsanity.com/paper/2506.14239/) (2025)

## Abstract (arXiv)

Theory of Mind (ToM) is the ability to understand human thinking and decision-making, an ability that plays a crucial role in social interaction between people, including linguistic communication. This paper investigates to what extent recent Large Language Models in the ChatGPT tradition possess ToM. We posed six well-known problems that address biases in human reasoning and decision making to two versions of ChatGPT and we compared the results under a range of prompting strategies. While the results concerning ChatGPT-3 were somewhat inconclusive, ChatGPT-4 was shown to arrive at the correct answers more often than would be expected based on chance, although correct answers were often arrived at on the basis of false assumptions or invalid reasoning.