LLMs achieve adult human performance on higher-order theory of mind
  tasks
Authors: name: Winnie Street, name: John Oliver Siy, name: Geoff Keeling, name: Adrien Baranes, name: Benjamin Barnett, name: Michael McKibben, name: Tatenda Kanyere, name: Alison Lentz, name: Blaise Aguera y Arcas, name: Robin I. M. Dunbar

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.