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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
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    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.

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