Intuitive psychology is a pillar of common-sense reasoning. The replication of this reasoning in machine intelligence is an important stepping-stone on the way to human-like artificial intelligence. Several recent tasks and benchmarks for examining this reasoning in Large-Large Models have focused in particular on belief attribution in Theory-of-Mind tasks. These tasks have shown both successes and failures. We consider in particular a recent purported success case, and show that small variations that maintain the principles of ToM turn the results on their head. We argue that in general, the zero-hypothesis for model evaluation in intuitive psychology should be skeptical, and that outlying failure cases should outweigh average success rates. We also consider what possible future successes on Theory-of-Mind tasks by more powerful LLMs would mean for ToM tasks with people.

翻译：直觉心理学是常识推理的基石。在机器智能中复现这种推理，是迈向类人人工智能的重要基石。近期针对大型语言模型（LLMs）中该推理能力的多项任务和基准测试，特别聚焦于心理理论任务中的信念归因。这些任务既展示了成功案例，也暴露了失败情形。我们特别关注一个近期声称成功的案例，并表明：保持心理理论原则的微小改动会彻底颠覆原有结果。我们认为，在直觉心理学的模型评估中，零假设应持怀疑态度，且离群失败案例应重于平均成功率。同时，我们探讨了未来更强大的LLMs在心理理论任务上可能取得的成功，对涉及人类受试者的心理理论任务所具有的潜在意义。