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Emergent analogical reasoning in large language models

Nature Human Behaviour volume 7, pages 1526–1541 (2023)Cite this article

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Abstract

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 Generative Pre-trained Transformer (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.

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Fig. 1: Summary of results.
Fig. 2: Matrix reasoning problems.
Fig. 3: Matrix reasoning results.
Fig. 4: Human performance for Digit Matrices versus Raven’s SPM.
Fig. 5: Letter string analogy problems.
Fig. 6: Letter string analogy results.
Fig. 7: Verbal analogy results.
Fig. 8: Story analogy results.

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Data availability

Data for all human behavioural experiments, along with the Digit Matrices, letter string analogy and UCLA VAT problem sets, can be downloaded from https://github.com/taylorwwebb/emergent_analogies_LLM. The four-term verbal analogy problem sets from Sternberg and Nigro17 and Jones et al.20, and the story analogy materials from Gentner et al.23, can be downloaded from http://cvl.psych.ucla.edu/resources/AnalogyInventory.zip. Information about the problem set of SAT four-term verbal analogies from Turney et al.18 can be found at https://aclweb.org/aclwiki/SAT_Analogy_Questions_(State_of_the_art).

Code availability

Code for all simulations can be downloaded from https://github.com/taylorwwebb/emergent_analogies_LLM.

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Acknowledgements

We thank B. Snefjella and P. Turney for helpful feedback and discussions. Preparation of this paper was supported by NSF grant IIS-1956441 and AFOSR MURI grant FA9550-22-1-0380 to H.L. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, University of California, Los Angeles, CA, USA

    Taylor Webb, Keith J. Holyoak & Hongjing Lu

  2. Department of Statistics, University of California, Los Angeles, CA, USA

    Hongjing Lu

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Contributions

T.W., K.J.H. and H.L. conceived the project and planned experiments. T.W. implemented experiments and analysed results. T.W., K.J.H. and H.L. drafted the manuscript.

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Correspondence to Taylor Webb.

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Webb, T., Holyoak, K.J. & Lu, H. Emergent analogical reasoning in large language models. Nat Hum Behav 7, 1526–1541 (2023). https://doi.org/10.1038/s41562-023-01659-w

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