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The escalating debate on AI's capabilities warrants developing reliablemetrics to assess machine "intelligence". Recently, many anecdotal exampleswere used to suggest that newer large language models (LLMs) like ChatGPT andGPT-4 exhibit Neural Theory-of-Mind (N-ToM); however, prior work reachedconflicting conclusions regarding those abilities. We investigate the extent ofLLMs' N-ToM through an extensive evaluation on 6 tasks and find that while LLMsexhibit certain N-ToM abilities, this behavior is far from being robust. Wefurther examine the factors impacting performance on N-ToM tasks and discoverthat LLMs struggle with adversarial examples, indicating reliance on shallowheuristics rather than robust ToM abilities. We caution against drawingconclusions from anecdotal examples, limited benchmark testing, and usinghuman-designed psychological tests to evaluate models.

Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models