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Emergent analogical reasoning in large language models

Author

Listed:
  • Taylor Webb

    (University of California)

  • Keith J. Holyoak

    (University of California)

  • Hongjing Lu

    (University of California
    University of California)

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.

Suggested Citation

  • Taylor Webb & Keith J. Holyoak & Hongjing Lu, 2023. "Emergent analogical reasoning in large language models," Nature Human Behaviour, Nature, vol. 7(9), pages 1526-1541, September.
  • Handle: RePEc:nat:nathum:v:7:y:2023:i:9:d:10.1038_s41562-023-01659-w
    DOI: 10.1038/s41562-023-01659-w
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    Cited by:

    1. Siting Estee Lu, 2024. "Strategic Interactions between Large Language Models-based Agents in Beauty Contests," Papers 2404.08492, arXiv.org, revised Oct 2024.
    2. James W. A. Strachan & Dalila Albergo & Giulia Borghini & Oriana Pansardi & Eugenio Scaliti & Saurabh Gupta & Krati Saxena & Alessandro Rufo & Stefano Panzeri & Guido Manzi & Michael S. A. Graziano & , 2024. "Testing theory of mind in large language models and humans," Nature Human Behaviour, Nature, vol. 8(7), pages 1285-1295, July.
    3. Jeongbin Kim & Matthew Kovach & Kyu-Min Lee & Euncheol Shin & Hector Tzavellas, 2024. "Learning to be Homo Economicus: Can an LLM Learn Preferences from Choice," Papers 2401.07345, arXiv.org.
    4. Jian-Qiao Zhu & Haijiang Yan & Thomas L. Griffiths, 2024. "Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice," Papers 2405.19313, arXiv.org.
    5. Yan Leng & Yuan Yuan, 2023. "Do LLM Agents Exhibit Social Behavior?," Papers 2312.15198, arXiv.org, revised Oct 2024.

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