The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Galactica AI model was trained on scientific knowledge – but it spat out alarmingly plausible nonsense
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In particular, language models trained with RLHF can sound confident even when they are incorrect (Snoswell and Burgess, 2022) which can lead humans to provide more positive feedback (Bowman et al., 2022).”
From Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback (2023) · cites this paper as Snoswell and Burgess, 2022
Citations
Cites
Global warming transforms coral reef assemblages
Hughes, T. P., Kerry, J. T., Baird, A. H., Connolly, S. R., Dietzel, A., Eakin, C. M., Heron, S. F., Hoey, A. S., Hoogenboom, M. O., Liu, G., et al. (2018) · Nature · doi:10.1038/s41586-018-0041-2 · Cited by 1,745
On misogynoir: citation, erasure, and plagiarism
Bailey, M. (2018) · Feminist Media Studies · doi:10.1080/14680777.2018.1447395 · Cited by 659
Chakravartty, P., Kuo, R., Grubbs, V., & McIlwain, C. (2018) · Journal of Communication · doi:10.1093/joc/jqy003 · Cited by 643
Citation inequity and gendered citation practices in contemporary physics
Teich, E. G., Kim, J. Z., Lynn, C. W., Simon, S. C., Klishin, A. A., Szymula, K. P., Srivastava, P., Bassett, L. C., Zurn, P., Dworkin, J. D., et al. (2022) · Nature Physics · doi:10.1038/s41567-022-01770-1 · Cited by 146
Cited by
Generative AI in Higher Education: The ChatGPT Effect
Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Casper, S., Davies, X., Shi, C., Gilbert, T. K., Scheurer, J., Rando, J., Freedman, R., Korbak, T., Lindner, D., Freire, P., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15217 · Cited by 101
Application of artificial intelligence to overcome clinical information overload in cancer
Stenzl, A., Ghith, J., & Schijvenaars, B. J. A. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780192867636.003.0022
Metadata
- Authors
- Snoswell, A., & Burgess, J.
- Affiliations
- Queensland University of Technology (AU)
- Publisher
- The Conversation Media Group Ltd
- Published
- 2022-11-29
- OpenAlex
- W4413597079
- Cited by
- 2
- References
- 4
- Citations by year
- 2025: 1 · 2024: 1
- Type
- other
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- large language models, scientific knowledge, hallucinations, factual errors, AI training data
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Galactica AI model was trained on scientific knowledge – but it spat out alarmingly plausible nonsense. https://app.cochat.ai/papers/10.64628/aa.ufydesxv5
- The paper
- Snoswell, A., & Burgess, J. (2022). The Galactica AI model was trained on scientific knowledge – but it spat out alarmingly plausible nonsense. https://doi.org/10.64628/aa.ufydesxv5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
