LLMs Achieve Adult Human Performance on Higher-Order Theory of Mind Tasks

Winnie Street^1*John Oliver Siy²Geoff Keeling³Adrien Baranes⁴Benjamin Barnett⁵Michael McKibben⁶Tatenda Kanyere⁷Blaise Agüera y Arcas⁸Robin Dunbar⁹

• ¹Google (United Kingdom), London, United Kingdom
• ²Google, Cambridge, MA, United States
• ³Google, London, United Kingdom
• ⁴Google DeepMind, London, United Kingdom
• ⁵Google, New York, United States
• ⁶Applied Physics Lab, Johns Hopkins University, Maryland, United States
• ⁷Work done at Google Research via Harvey Nash, London, United Kingdom
• ⁸Google, Seattle, United States
• ⁹Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

This paper examines the extent to which large language models (LLMs) are able to perform tasks which require higher-order theory of mind (ToM)–the human ability to reason about multiple mental and emotional states in a recursive manner (e.g. I think that you believe that she knows). This paper builds on prior work by introducing a handwritten test suite – Multi-Order Theory of Mind Q&A – and using it to compare the performance of five LLMs of varying sizes and training paradigms to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on our ToM tasks overall, and that GPT-4 exceeds adult performance on 6th order inferences. Our results suggest that there is an interplay between model size and finetuning for higher-order ToM performance, and that the linguistic abilities of large models may support more complex ToM inferences. Given the important role that higher-order ToM plays in group social interaction and relationships, these findings have significant implications for the development of a broad range of social, educational and assistive LLM applications.