IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v623y2023i7987d10.1038_s41586-023-06647-8.html
   My bibliography  Save this article

Role play with large language models

Author

Listed:
  • Murray Shanahan

    (Google DeepMind
    Imperial College London)

  • Kyle McDonell

    (Eleuther AI)

  • Laria Reynolds

    (Eleuther AI)

Abstract

As dialogue agents become increasingly human-like in their performance, we must develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. Here we foreground the concept of role play. Casting dialogue-agent behaviour in terms of role play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models that they in fact lack. Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness.

Suggested Citation

  • Murray Shanahan & Kyle McDonell & Laria Reynolds, 2023. "Role play with large language models," Nature, Nature, vol. 623(7987), pages 493-498, November.
    • Handle: RePEc:nat:nature:v:623:y:2023:i:7987:d:10.1038_s41586-023-06647-8
    DOI: 10.1038/s41586-023-06647-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-023-06647-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41586-023-06647-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen Gao & Xiaochong Lan & Nian Li & Yuan Yuan & Jingtao Ding & Zhilun Zhou & Fengli Xu & Yong Li, 2024. "Large language models empowered agent-based modeling and simulation: a survey and perspectives," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.
    2. Holtdirk, Tobias & Assenmacher, Dennis & Bleier, Arnim & Wagner, Claudia, 2024. "Fine-Tuning Large Language Models to Simulate German Voting Behaviour (Working Paper)," OSF Preprints udz28, Center for Open Science.
    3. Zhen Wang & Ruiqi Song & Chen Shen & Shiya Yin & Zhao Song & Balaraju Battu & Lei Shi & Danyang Jia & Talal Rahwan & Shuyue Hu, 2024. "Large Language Models Overcome the Machine Penalty When Acting Fairly but Not When Acting Selfishly or Altruistically," Papers 2410.03724, arXiv.org, revised Oct 2024.
    4. Ivanov, Stanislav & Soliman, Mohammad & Tuomi, Aarni & Alkathiri, Nasser Alhamar & Al-Alawi, Alamir N., 2024. "Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour," Technology in Society, Elsevier, vol. 77(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:623:y:2023:i:7987:d:10.1038_s41586-023-06647-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.