LLMs achieve adult human performance on higher-order theory of mind tasks
<|reference_start|>LLMs achieve adult human performance on higher-order theory of mind tasks: This paper examines the extent to which large language models (LLMs) have developed 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 to a newly gathered adult human benchmark. We find that GPT-4 and Flan-PaLM reach adult-level and near adult-level performance on 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 the realisation of ToM abilities, and that the best-performing LLMs have developed a generalised capacity for ToM. Given the role that higher-order ToM plays in a wide range of cooperative and competitive human behaviours, these findings have significant implications for user-facing LLM applications.<|reference_end|>
@article{street2024llms,
    title={LLMs achieve adult human performance on higher-order theory of mind
  tasks},
    author={Winnie Street, John Oliver Siy, Geoff Keeling, Adrien Baranes,
  Benjamin Barnett, Michael McKibben, Tatenda Kanyere, Alison Lentz, Blaise
  Aguera y Arcas, Robin I. M. Dunbar},
    journal={arXiv preprint arXiv:2405.18870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18870},
    primaryClass={cs.AI cs.CL cs.HC}
}