Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

• URL: http://arxiv.org/abs/2302.08399v3
• Date: Mon, 20 Feb 2023 03:46:17 GMT
• Title: Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks
• Authors: Tomer Ullman
• Abstract summary: Theory-of-Mind tasks have shown both successes and failures. 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.
• Score: 3.3178024597495903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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