LLMs achieve adult human performance on higher-order theory of mind tasks.

ABSTRACT: 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 finetunin

SUBMITTER: Street W 

PROVIDER: S-EPMC12808479 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature