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Theory of mind (ToM) evaluations currently focus on testing models usingpassive narratives that inherently lack interactivity. We introduce FANToM, anew benchmark designed to stress-test ToM within information-asymmetricconversational contexts via question answering. Our benchmark draws uponimportant theoretical requisites from psychology and necessary empiricalconsiderations when evaluating large language models (LLMs). In particular, weformulate multiple types of questions that demand the same underlying reasoningto identify illusory or false sense of ToM capabilities in LLMs. We show thatFANToM is challenging for state-of-the-art LLMs, which perform significantlyworse than humans even with chain-of-thought reasoning or fine-tuning.

FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions