论文标题
大语言模型中的新兴类比推理
Emergent Analogical Reasoning in Large Language Models
论文作者
论文摘要
大型语言模型的最新出现激起了人们关于人类认知能力是否可能在此类通用模型中出现的辩论。特别令人感兴趣的是这些模型在没有任何直接训练的情况下推理新问题的能力。在人类的认知中，这种能力与类比的推理能力紧密相关。在这里，我们对人类推理者和大型语言模型（GPT-3的Text-Davinci-003变体）进行了直接比较，包括基于Raven标准渐进式矩阵的规则结构的非视觉矩阵推理任务，包括非视觉矩阵推理任务。我们发现，在大多数情况下，GPT-3表现出令人惊讶的强大能力吸引，匹配甚至超过人类能力的能力。 GPT-4的初步测试表明性能更好。我们的结果表明，诸如GPT-3之类的大型语言模型已经获得了新兴的能力，可以找到针对广泛类比问题的零摄像解决方案。
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. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings; preliminary tests of GPT-4 indicated even better performance. 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.
