Position: Theory of Mind Benchmarks are Broken for Large Language Models

This position paper argues that the majority of theory of mind benchmarks are broken because of their inability to directly test how large language models (LLMs) adapt to new partners. This problem stems from the fact that theory of mind benchmarks for LLMs are overwhelmingly inspired by the methods used to test theory of mind in humans and fall victim to a fallacy of attributing human-like qualities to AI agents. We expect that humans will engage in a consistent reasoning process across various questions about a situation, but this is known to not be the case for current LLMs. Most theory of mind benchmarks only measure what we call literal theory of mind: the ability to predict the behavior of others. Measuring this kind of reasoning is very informative in testing the ability of agents with self-consistent reasoning. However, it is important to note the distinction between this and what we actually care about when this self-consistency cannot be taken for granted. We call this functional theory of mind: the ability to adapt to agents in-context following a rational response to predictions about their behavior. We find that top performing open source LLMs may display strong capabilities in literal theory of mind, depending on how they are prompted, but seem to struggle with functional theory of mind -- even when partner policies are exceedingly simple. Simply put, strong literal theory of mind performance does not necessarily imply strong functional theory of mind performance. Achieving functional theory of mind, particularly over long interaction horizons with a partner, is a significant challenge deserving a prominent role in any meaningful LLM theory of mind evaluation.