Large language models (LLMs) do not distinguish between fact and fiction. They will return an answer to almost any prompt, yet factually incorrect responses are commonplace. To ensure our use of LLMs does not degrade science, we must use them as zero-shot translators: to convert accurate source material from one form to another.
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Acknowledgements
This work has been supported through research funding provided by the Wellcome Trust (grant no. 223765/Z/21/Z), Sloan Foundation (grant no. G-2021-16779), the Department of Health and Social Care, and Luminate Group to support the Trustworthiness Auditing for AI project and Governance of Emerging Technologies research programme at the Oxford Internet Institute, University of Oxford. The funders had no role in the decision to publish or the preparation of this manuscript.
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B.M. and S.W. declare no competing interests. C.R. was also an employee of Amazon Web Services during part of the writing of this article. He did not contribute to this article in his capacity as an Amazon employee.
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Mittelstadt, B., Wachter, S. & Russell, C. To protect science, we must use LLMs as zero-shot translators. Nat Hum Behav 7, 1830–1832 (2023). https://doi.org/10.1038/s41562-023-01744-0
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DOI: https://doi.org/10.1038/s41562-023-01744-0
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