Emergent Analogical Reasoning in Large Language Models

Understand

The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data.

• Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training.
• In human cognition, this capacity is closely tied to an ability to reason by analogy.
• Here, we performed a direct comparison between human reasoners and a large language model (the text-davinci-003 variant of GPT-3) on a range of analogical tasks, including a non-visual matrix reasoning task based on the rule structure of Raven's Standard Progressive Matrices.