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 novel text-based matrix reasoning task closely modeled on
Raven's Progressive Matrices. We found that GPT-3 displayed a surprisingly
strong capacity for abstract pattern induction, matching or even surpassing
human capabilities in most settings. Our results indicate that large language
models such as GPT-3 have acquired an emergent ability to find zero-shot
solutions to a broad range of analogy problems.

本研究探讨了大型语言模型（比如 GPT-3）在模拟人类的模拟推理能力方面的表现，特别是它在没有直接训练的情况下是否能够进行零样本推理。研究发现，GPT-3 在抽象模式归纳方面具有出色的能力，并能够在大多数场景中匹配或超过人类的能力。因此，大型语言模型能够在许多类比问题中找到零样本解决方案。