The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators
Open accessAbstract
Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the release of OpenAI’s ChatGPT, a proprietary large language model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review the risks of relying on proprietary software and survey the first crop of open-source projects of comparable architecture and functionality. The main contribution of this paper is to show that openness is differentiated, and to offer scientific documentation of degrees of openness in this fast-moving field. We evaluate projects in terms of openness of code, training data, model weights, RLHF data, licensing, scientific documentation, and access methods. We find that while there is a fast-growing list of projects billing themselves as ‘open source’, many inherit undocumented data of dubious legality, few share the all-important instruction-tuning (a key site where human annotation labour is involved), and careful scientific documentation is exceedingly rare. Degrees of openness are relevant to fairness and accountability at all points, from data collection and curation to model architecture, and from training and fine-tuning to release and deployment.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“For many scientists and public interest practitioners, this lack of transparency is at best concerning, and often a reason to avoid such models in their work altogether [38, 46].”
From Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings (2024) · cites this paper as [38]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“For one, we are already seeing a strong trend towards selective and self-serving forms of openness, both in release strategies and in empirical surveys of model openness ([32, 53, 60], and see below).”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Open generative AI systems are on the rise, with small players and academic initiatives leading the way in open innovation and scientific documentation [20, 32, 61] and several larger corporations joining the fray by releasing models billed as 'open'.”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“The multi-faceted nature of openness is also seen in initiatives that aim to codify transparency from upstream resources to downstream uses [8] and in a proposal like the Linux Foundation's model openness framework [59], which shows many likenesses to the dimensions we use here and introduced in [32].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [32]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
The protein data bank: A computer-based archival file for macromolecular structures
Bernstein, F. C., Koetzle, T. F., Williams, G. J. B., Meyer, E. F., Brice, M. D., Rodgers, J. R., Kennard, O., Shimanouchi, T., & Tasumi, M. (1977) · Journal of Molecular Biology · doi:10.1016/s0022-2836(77)80200-3 · Cited by 8,574
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 2×
“It is one of the achievements of publicly funded science that it can afford to not jump on the bandwagon and instead make room for reflection [2, 5].”
BackgroundLLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1×
“system predicting the structure of nearly all known proteins [53], where decades of prior work had reached a comparatively meagre 17%) has only been possible thanks to openly deposited structural data in the Protein Data Bank that goes back half a century [3].”
BackgroundModel Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681
Self-Instruct: Aligning Language Models with Self-Generated Instructions
Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.754 · Cited by 1,243
Frankfurt, H. G. (2005) · doi:10.1515/9781400826537 · Cited by 1,175
Flagged first, then by citations
Research works printed but not linked: 3
- Ivan Illich. 1973. Tools for conviviality. Harper & Row, New York.
- Nathan Lambert, Louis Castricato, Leandro von Werra, and Alex Havrilla. 2022. Illustrating Reinforcement Learning from Human Feedback (RLHF). Hugging Face Blog (2022).
- Dan McQuillan. 2022. Resisting AI: an anti-fascist approach to artificial intelligence. Bristol University Press, Bristol, UK. OCLC: on1328026349.
Cited by
Sorted by citations
Items 101–113 of 113
The politics of newswriting : three essays on how journalists cover politics
Shen, R. (2024) · theses.fr (ABES) · doi:10.70675/01d0eae8z52b1z4d10z97d8z8a40763bb73a
Scheuerman, M. K., Hutiri, W. T., Rahmattalabi, A., Matthews, V., Xiang, A., & Andrews, J. T. A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790364 · Cited by 1
Dhunnoo, P., Meskó, B., McGuigan, K., & McCann, M. (2026) · doi:10.2196/preprints.100738
Why Open a Generative AI Model? A Typology Based on What is Open and What is Not
Viseur, R., & Jullien, N. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-26836-5_14
Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences
Ferrari, N. C., Conklin, E. E., Fitch, A., Gannon, D. G., Macedo, R., Place, A., Sachs, H. R., Seo, E., Weldy, M. J., & Betts, M. G. (2026) · Ecology and Evolution · doi:10.1002/ece3.74118
Don't Trust the Process: When Verifiability Undermines AI Accountability
Paris, T., Moon, A., & Guo, J. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812373
REST-at: An LLM-Based Tool for Automating Traceability between Requirements and Test Cases
Leon-Quinstedt, N., Lindgren, B., Yurdakul, M., & Neto, F. G. D. O. (2026) · doi:10.1145/3793654.3793746 · Cited by 1
For robust research, center values, not technology
Dingemanse, M., & Cuskley, C. F. (2026) · Behavioral and Brain Sciences · doi:10.1017/s0140525x26104816
Self-citationNonefinity Agent: A Configurable Platform for Knowledge-Augmented Reasoning
Nguyễn, H., Nguyễn, T., Bùi, M., & Hà, T. (2026) · International Conference on Digital Transformation, Innovation & Sustainable Development (DTISD) · doi:10.1109/dtisd69661.2026.11668278
Human-centric skills are essential for the responsible and rigorous application of AI in ecology
Kendall-Bar, J. M., Payne, A. R., Czapanskiy, M. F., Fox, N., Leivesley, J. A., Alksne, M. N., French, B., Malivert, A., O’Leary, G., Biswas, A., et al. (2026) · Environmental Data Science · doi:10.1017/eds.2026.10042
Dhunnoo, P., McGuigan, K., Meskó, B., O’Rourke, V., & McCann, M. (2026) · JMIR Human Factors · doi:10.2196/100738
Data Quality: Models, Measures, and Tools
Chen, H., 沙科伟, Zhou, Y., & Tu, F. (2026) · Springer handbooks · doi:10.1007/978-3-031-92372-2_55
Alharbi, M., & Alhargan, A. (2026) · IEEE Access · doi:10.1109/access.2026.3742992
Metadata
- Authors
- Liesenfeld, A., Lopez, A., & Dingemanse, M.
- Affiliations
- Radboud University Nijmegen (NL)
- Venue
- Proceedings of the 5th International Conference on Conversational User Interfaces
- Publisher
- ACM
- Published
- 2023-07-19
- Pages
- 1-6
- OpenAlex
- W4384261711
- Cited by
- 111
- References
- 53
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 52 · 2023: 12
- FWCI
- 27.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Conversational User Interfaces (CUI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.Vidi.185.205)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- ChatGPT, instruction-tuned language models, open-source large language models, openness, transparency, accountability, reinforcement learning from human feedback, model weights, licensing, scientific documentation, proprietary software
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
