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Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
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The escalating debate on AI's capabilities warrants developing reliable metrics to assess machine "intelligence". Recently, many anecdotal examples were used to suggest that newer large language models (LLMs) like ChatGPT and GPT-4 exhibit Neural Theory-of-Mind (N-ToM); however, prior work reached conflicting conclusions regarding those abilities. We investigate the extent of LLMs' N-ToM through an extensive evaluation on 6 tasks and find that while LLMs exhibit certain N-ToM abilities, this behavior is far from being robust. We further examine the factors impacting performance on N-ToM tasks and discover that LLMs struggle with adversarial examples, indicating reliance on shallow heuristics rather than robust ToM abilities. We caution against drawing conclusions from anecdotal examples, limited benchmark testing, and using human-designed psychological tests to evaluate models.
Forward citations
Cited by 5 Pith papers
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LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
A benchmark for LLM agents in partially observable joint decision-making reveals that deliberation challenges current models but can enable reflection and error correction.
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OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
OSCToM uses RL-guided generation with an extended DSL and surrogate models to create nested belief conflict tasks, raising FANToM accuracy from 0.2% to 76% while being 6x more efficient.
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Don't Make the LLM Read the Graph: Make the Graph Think
Belief graphs improve LLM 2nd-order theory-of-mind performance in Hanabi when they gate action selection (100% vs 20%) but are mostly decorative as prompt context for strong models, with model-family-specific planner ...
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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