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Using large language models in psychology

Nature Reviews Psychology volume 2, pages 688–701 (2023)Cite this article

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Abstract

Large language models (LLMs), such as OpenAI’s GPT-4, Google’s Bard or Meta’s LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to advance psychological measurement, experimentation and practice, they are not yet ready for many of the most transformative psychological applications — but further research and development may enable such use. Next, we examine four major concerns about the application of LLMs to psychology, and how each might be overcome. Finally, we conclude with recommendations for investments that could help to address these concerns: field-initiated ‘keystone’ datasets; increased standardization of performance benchmarks; and shared computing and analysis infrastructure to ensure that the future of LLM-powered research is equitable.

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Fig. 1: Examples of LLM functionality.
Fig. 2: Pre-training, fine-tuning and prompt-tuning of LLMs.

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Acknowledgements

This work was supported by the National Science Foundation under award numbers 1761179 and 2201928 (PI: D.S.Y.), by the National Institutes of Health under award numbers R01HD084772 (PI: D.S.Y.) and P2CHD042849 (Population Research Center), and by the William and Melinda Gates Foundation under awards INV-047751 and INV-004519 (PI: D.S.Y.). This work was also supported by an Advanced Research Fellowship from the Jacobs Foundation to D.S.Y., and the Institute for Human-Centered A.I. at Stanford University to J.C.E. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The authors also thank C. Smith for creating the original version of the figures included with the original submission. The glossary definitions were generated by GPT-4 in May 2023 and edited by the authors.

Author information

Author notes

  1. These authors contributed equally: Dorottya Demszky, Diyi Yang, David S. Yeager.

Authors and Affiliations

  1. Graduate School of Education, Stanford University, Stanford, CA, USA

    Dorottya Demszky

  2. Department of Computer Science, Stanford University, Stanford, CA, USA

    Diyi Yang

  3. Texas Behavioral Science and Policy Institute, University of Texas at Austin, Austin, TX, USA

    David S. Yeager, Christopher J. Bryan, Margarett Clapper, Cameron Hecht, Meghann Johnson, Michaela Jones, Nirel JonesMitchell & Desmond C. Ong

  4. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    David S. Yeager, Margarett Clapper, Cameron Hecht, Desmond C. Ong & James W. Pennebaker

  5. Department of Business, Government, and Society, University of Texas at Austin, Austin, TX, USA

    Christopher J. Bryan

  6. Google, LLC, Mountain View, CA, USA

    Susannah Chandhok, Danielle Krettek-Cobb & Leslie Lai

  7. Department of Psychology, Stanford University, Stanford, CA, USA

    Johannes C. Eichstaedt, Carol S. Dweck & James J. Gross

  8. Institute for Human-Centered AI, Stanford University, Stanford, CA, USA

    Johannes C. Eichstaedt

  9. Department of Psychology, University of Rochester, Rochester, NY, USA

    Jeremy Jamieson

Authors
  1. Dorottya Demszky
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  2. Diyi Yang
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  3. David S. Yeager
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  4. Christopher J. Bryan
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  5. Margarett Clapper
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  6. Susannah Chandhok
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  7. Johannes C. Eichstaedt
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  8. Cameron Hecht
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  9. Jeremy Jamieson
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  10. Meghann Johnson
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  11. Michaela Jones
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  12. Danielle Krettek-Cobb
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  13. Leslie Lai
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  14. Nirel JonesMitchell
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  15. Desmond C. Ong
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  16. Carol S. Dweck
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  17. James J. Gross
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  18. James W. Pennebaker
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Contributions

Lead authors D.D., D.Y. and D.S.Y. (equal contributions, listed alphabetically) conceived the paper, outlined and wrote the first draft, guided the co-authoring process, provided critical edits, conceived and supervised the creation of the figures, boxes and tables, and finalized the submitted version of the manuscript. Senior authors C.S.D., J.J.G. and J.W.P. (listed alphabetically) assisted in the outlining, organization, and conceptualization of the manuscript, boxes and tables and provided multiple rounds of critical edits. J.C.E. assisted with outlining the paper, wrote first drafts of key sections and edited the draft. All other authors assisted with the empirical examples, the conceptualization of the key arguments and conclusions in the paper and provided critical edits.

Corresponding authors

Correspondence to Dorottya Demszky, Diyi Yang or David S. Yeager.

Ethics declarations

Competing interests

J.W.P. is the CEO of Pennebaker Conglomerates, a company that sells natural language processing software and services. S.C. and L.L. are employees of Google LLC, which owns LLM technology. D.K.-C. was formerly an employee at Google LLC.

Peer review

Peer review information

Nature Reviews Psychology thanks William Brady and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Backpropagation

An algorithmic technique that utilizes a reverse pass through the network to calculate the contribution of each parameter to the prediction error and adjust them accordingly to improve performance.

Bag-of-words methods

A text representation technique that counts the frequency of words in a document, disregarding grammar and word order, such as the Linguistic Inquiry and Word Count algorithm.

Generative pre-trained transformer

A family of large language models developed by OpenAI and usually trained on massive datasets to generate contextually coherent text.

Machine learning

A subset of artificial intelligence that involves teaching computers to learn patterns and make decisions from data without explicit programming.

Neural network

A computational model inspired by the structure and function of biological neural networks used for tasks such as pattern recognition, classification and prediction.

Training data

The dataset used to train a machine learning model, consisting of input–output pairs that help the model to learn the underlying patterns and relationships.

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Demszky, D., Yang, D., Yeager, D.S. et al. Using large language models in psychology. Nat Rev Psychol 2, 688–701 (2023). https://doi.org/10.1038/s44159-023-00241-5

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