To protect science, we must use LLMs as zero-shot translators
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LLMs are infamous for making up references, providing inaccurate summaries, and making false claims that sound authoritative and convincing.
“LLMs are infamous for making up references (10), providing inaccurate summaries (35, 36), and making false claims that sound authoritative and convincing (8, 11, 12, 37).”
From Delving into LLM-assisted writing in biomedical publications through excess vocabulary (2025) · cites this paper as [8]
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Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
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“Downsizing the size and scope of models should also be considered, including restricting the types of valid prompts to areas for which the model has reliable training data or can pass an accepted confidence threshold 1 .”
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“…LLMs are infamous for making up references (Walters and Wilder, 2023), providing inaccurate summaries (Tang et al., 2024a; Kim et al., 2024), and making false claims that sound authoritative and convincing (Mittelstadt et al., 2023; Ji et al., 2023; Zhang et al., 2023; Zheng and Zhan, 2023).”
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Self-citation
- Authors
- Mittelstadt, B., Wachter, S., & Russell, C.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Mittelstadt, Brent
- Venue
- Nature Human Behaviour
- Published
- 2023-11-20
- Volume
- 7
- Issue
- 11
- Pages
- 1830-1832
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W4388824189
- Cited by
- 58
- References
- 12
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 18 · 2023: 1 · 2022: 1
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Alfred P. Sloan Foundation (G-2021-16779)
- Wellcome Trust (223765/Z/21/Z)
- Department of Health and Social Care Luminate Group
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- large language models, factual accuracy, zero-shot translation, fact-checking, scientific integrity, misinformation, knowledge distillation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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