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.
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For many scientists and public interest practitioners, lack of transparency is a reason to avoid closed models in their work.
“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]
The Introduction says proprietary systems lack transparent ethical oversight and that working scientists call for avoiding proprietary models. Section 1.1 explains why openness matters for research. The paper does not establish that many scientists or public-interest practitioners avoid these models in their work, or how often they do so. (LLM, conf=0.82)
Experimental — accuracy not yet measuredWe 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.
“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]
Open generative AI systems are on the rise, with small players and academic initiatives leading the way in open innovation and scientific documentation and several larger corporations joining the fray by releasing models billed as open.
“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 multi-faceted nature of openness is also seen in initiatives that aim to codify transparency from upstream resources to downstream uses and in the Linux Foundation's model openness framework.
“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]
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ChatGPT for Text Annotation? Mind the Hype!Preprint
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Performance of Publicly Available Large Language Models on Internal Medicine Board-style Questions
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Old Moats for New Models: Openness, Control, and Competition in Generative AI
Azoulay, P., Krieger, J., & Nagaraj, A. (2024) · National Bureau of Economic Research · doi:10.3386/w32474 · Cited by 28 · see citing works
Rane, N. L., Choudhary, S., & Rane, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4681720 · Cited by 23 · see citing works
Osborne, C., Ding, J., & Kirk, H. R. (2024) · Journal of Computational Social Science · doi:10.1007/s42001-024-00300-8 · Cited by 22 · see citing works
Computational reproducibility in computational social science
Schoch, D., Chan, C.-H., Wagner, C., & Bleier, A. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00514-w · Cited by 22 · see citing works
Zahid, I. A., & Joudar, S. S. (2023) · Applied Data Science and Analysis · doi:10.58496/adsa/2023/014 · Cited by 21 · see citing works
How to Regulate Large Language Models for Responsible AI
Berengueres, J. O. L. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3403681 · Cited by 18 · see citing works
Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning
Alizadeh, M., Kubli, M., Samei, Z., Dehghani, S., Zahedivafa, M., Bermeo, J. D., Korobeynikova, M., & Gilardi, F. (2023) · arXiv · doi:10.48550/arxiv.2307.02179 · Cited by 18 · see citing works
Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings
Wolfe, R., Slaughter, I., Han, B., Wen, B., Yang, Y., Rosenblatt, L., Herman, B., Brown, E., Qu, Z., Weber, N., et al. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658966 · Cited by 17 · see citing works
Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering
Addlesee, A., Sieińska, W., Gunson, N., García, D. H., Dondrup, C., & Lemon, O. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.22 · Cited by 19 · see citing works
Mapping the individual, social and biospheric impacts of Foundation Models
Hernández, A. D., Krishna, S., Perini, A., Katell, M. A., Bennett, S., Borda, A., Hashem, Y., Hadjiloizou, S., Mahomed, S., Jayadeva, S., et al. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658939 · Cited by 20 · see citing works
Large language models as models of human cognitionPreprint
Frank, M. C. (2023) · doi:10.31234/osf.io/wxt69 · Cited by 17 · see citing works
Wu, L., Xu, J., Thakkar, S., Gray, M., Qu, Y., Li, D., & Tong, W. (2024) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2024.105613 · Cited by 16 · see citing works
Can Generative AI Chatbots Promote Second Language Acquisition? A Meta‐Analysis
Li, M., Wang, Y., & Yang, X. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70060 · Cited by 28 · see citing works
The Art of Talking Machines: A Comprehensive Literature Review of Conversational User Interfaces
Namvarpour, M., & Razi, A. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736621 · Cited by 16 · see citing works
Macanovic, A., & Przepiorka, W. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02381-9 · Cited by 17 · see citing works
Empowering financial futures: Large language models in the modern financial landscape
Cao, X., Li, S., Katsikis, V. N., Khan, A. T., He, H., Liu, Z., Zhang, L., & Peng, C. (2024) · EAI Endorsed Transactions on AI and Robotics · doi:10.4108/airo.6117 · Cited by 15 · see citing works
ChatGPT in the public eye: Ethical principles and generative concerns in social media discussions
Cohen, M., Khavkin, M., Davidow, D. M., & Toch, E. (2024) · New Media & Society · doi:10.1177/14614448241279034 · Cited by 19 · see citing works
Large Language Models in Neurological Practice: Real-World Study
Maiorana, N. V., Marceglia, S. R. F., Treddenti, M., Tosi, M., Guidetti, M., Creta, M. F., Bocci, T., Oliveri, S., Boneschi, F. M., & Priori, A. (2025) · Journal of Medical Internet Research · doi:10.2196/73212 · Cited by 14 · see citing works
Doctoral Programme in Education Science Universitas Pendidikan Ganesha, S., & Turmuzi, M. (2024) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2024.14.5.2097 · Cited by 17 · see citing works
Leivada, E., Marcus, G., Günther, F., & Murphy, E. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2025.0008 · Cited by 20 · see citing works
Smit, R., Smits, T., & Merrill, S. (2024) · Memory Studies Review · doi:10.1163/29498902-202400015 · Cited by 12 · see citing works
Large language models are better than theoretical linguists at theoretical linguistics
Ambridge, B., & Blything, L. P. (2024) · Theoretical Linguistics · doi:10.1515/tl-2024-2002 · Cited by 12 · see citing works
Rane, N., Choudhary, S. P., & Rane, J. (2024) · Social Science Research Network · doi:10.2139/ssrn.4681733 · Cited by 9 · see citing works
Navigating the Unknown: Anticipating Concerns and Gaps in Generative AI Research
Parker, L., Hayes, J., Loper, A. J., & Karakas, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-65691-0_1 · Cited by 10 · see citing works
Prompting for pedagogy? Australian F-10 teachers’ generative AI prompting use cases
Crosthwaite, P. R., Smala, S., & Spinelli, F. (2024) · The Australian Educational Researcher · doi:10.1007/s13384-024-00787-0 · Cited by 18 · see citing works
Language-Models-as-a-Service: Overview of a New Paradigm and its Challenges
La Malfa, E., Petrov, A., Frieder, S., Weinhuber, C., Burnell, R., Nazar, R., Cohn, A., Shadbolt, N., & Wooldridge, M. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15865 · Cited by 9 · see citing works
EX-CODE: A Robust and Explainable Model to Detect AI-Generated Code
Bulla, L., Midolo, A., Mongiovı̀, M., & Tramontana, E. (2024) · Information · doi:10.3390/info15120819 · Cited by 10 · see citing works
Algorithmic bias: sexualized violence against women in GPT-3 models
Wyer, S., & Black, S. (2025) · AI and Ethics · doi:10.1007/s43681-024-00641-0 · Cited by 9 · see citing works
HyperPIE: Hyperparameter Information Extraction from Scientific Publications
Saier, T., Ohta, M., Asakura, T., & Färber, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56060-6_17 · Cited by 10 · see citing works
A Steampunk Critique of Machine Learning Acceleration
Cremaschi, M., Dorfmann, M., & Angeli, A. D. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660688 · Cited by 9 · see citing works
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · doi:10.31219/osf.io/tg79n · Cited by 8 · see citing works
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · doi:10.31234/osf.io/d695y · Cited by 8 · see citing works
Chandra, R., Chaudhari, A., & Rayavarapu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3585629 · Cited by 8 · see citing works
Toward Data Sovereignty: Justice-oriented and Community-based AI Education
Moudgalya, S. K., & Swaminathan, S. (2024) · Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) · doi:10.1145/3653666.3656107 · Cited by 8 · see citing works
Luna, D. S., Broer, I., Bilandzić, H., Taddicken, M., Schuller, B. W., & Bürger, M. (2025) · Public Understanding of Science · doi:10.1177/09636625251328854 · Cited by 6 · see citing works
Weichselbraun, A., Süsstrunk, N., Waldvogel, R., Glatzl, A., Brașoveanu, A., & Scharl, A. (2024) · Future Internet · doi:10.3390/fi16050144 · Cited by 6 · see citing works
Exposing the Ideology of Large Language Models with Creative Practices
Cremaschi, M., Grigis, P., Dorfmann, M., & Angeli, A. D. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735715 · Cited by 5 · see citing works
ChatGPT in Academia: A Holistic Examination of Student Performance and Future Implications
Dahlan, M. A., Omar, R., & Kamarudin, S. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-62102-4_51 · Cited by 5 · see citing works
Large Language Models to process, analyze, and synthesize biomedical texts – a scoping reviewPreprint
Doneva, S. E., Qin, S., Sick, B., Ellendorff, T., Goldman, J.-P., Schneider, G., & Ineichen, B. V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.19.588095 · Cited by 5 · see citing works
Schepens, J. J., Woloszyn, H., Marx, N., & Gagl, B. (2023) · doi:10.31234/osf.io/gm9b6 · Cited by 4 · see citing works
Neural text embeddings in psychological research: A guide with examples in R.
Teitelbaum, L., & Simchon, A. (2025) · Psychological Methods · doi:10.1037/met0000768 · Cited by 3 · see citing works
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ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling
Fernandes, T., Nisi, V., Nunes, N., & James, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-74353-5_6 · Cited by 3 · see citing works
PTB-FLA development paradigm adaptation for ChatGPT
Popovic, M. V., Popović, M., Kaštelan, I., Djukic, M. M., & Basicevic, I. V. (2024) · Computer Science and Information Systems · doi:10.2298/csis231224036p · Cited by 3 · see citing works
Zhao, Z., Bangash, A. A., Cogo, F. R., Adams, B., & Hassan, A. E. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3533972 · Cited by 4 · see citing works
Data Dysphoria: The Governance Challenge Posed by Large Learning ModelsPreprint
Aaronson, S. A. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4554580 · Cited by 3 · see citing works
Old Moats for New Models: Openness, Control, and Competition in Generative Artificial Intelligence
Azoulay, P., Krieger, J., & Nagaraj, A. (2025) · Entrepreneurship and Innovation Policy and the Economy · doi:10.1086/732852 · Cited by 2 · see citing works
Learning from Machinery Misspeaks: Cognitive Basis of Ableism and Bias in AI-Generated Content
Abdelzaher, E. M., & Alshammari, S. (2025) · Corpus Pragmatics · doi:10.1007/s41701-025-00216-2 · Cited by 2 · see citing works
Toward Open-Source AI Systems as Digital Public Goods: Definitions, Hopes and Challenges
Gimpel, L. (2024) · Springer eBooks · doi:10.1007/978-3-031-61187-2_8 · Cited by 2 · see citing works
Can Language Models Trained on Written Monologue Learn to Predict Spoken Dialogue?
Umair, M., Mertens, J. B., Warnke, L., & de Ruiter, J. P. (2024) · Cognitive Science · doi:10.1111/cogs.70013 · Cited by 3 · see citing works
Generative AI and Research IntegrityPreprint
Dingemanse, M. (2024) · doi:10.31219/osf.io/2c48n · Cited by 2 · see citing works
Self-citationInquiry into the Paradox of an ICT Skills Gap
Brand, M., Greyling, J., & Calitz, A. P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82478-4_3 · Cited by 1 · see citing works
Making manual scoring of typed transcripts a thing of the past: a commentary on Herrmann (2025)
Bosker, H. R. (2025) · Speech Language and Hearing · doi:10.1080/2050571x.2025.2514395 · Cited by 1 · see citing works
Linkedin Post Generator Using Generative AI
Saini, M. L., Shrimal, V. M., Sati, D. C., Perla, I., & Garg, A. (2025) · World Skills Conference on Universal Data Analytics and Sciences (WorldSUAS) · doi:10.1109/worldsuas66815.2025.11199046 · Cited by 1 · see citing works
Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making
Chappidi, S., Singh, J., & Krauze, A. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791428 · Cited by 3 · see citing works
Algorithm-Assisted Inference and the Future of Official Statistics
Allorant, A., & Smith, P. A. (2026) · Journal of Official Statistics · doi:10.1177/0282423x261443590 · Cited by 1 · see citing works
Language Classroom Management: Underprivileges of ChatGPT
Hayat, A. (2025) · Journal of Management World · doi:10.53935/jomw.v2024i4.632 · Cited by 2 · see citing works
Responsible AI for Government Program Evaluation and Performance Audits
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Trustworthiness Evaluation of Large Language Models
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ChapGTP, ILLC’s Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation
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Whither developmental psycholinguistics?Preprint
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Is it time for the neurologist to use Large Language Models in everyday practice?Preprint
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Профессиональное самоопределение как компонент профессионального становления личности
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User Issues and Concerns in Generative AI: A Mixed-Methods Analysis of App Reviews
Bracamonte, V., Löbner, S., Tronnier, F., Lieberknecht, A.-K., & Pape, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82633-7_19
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ChatGPT in the Assessment of Speaking
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Large Language Models Readability Classification: A Variability Analysis of Sources and MetricsPreprint
de Matos, H. G. C., Wasmann, J.-W. A., Morata, T. C., Alvarenga, K. D. F., & JACOB, L. C. B. (2026) · medRxiv · doi:10.64898/2026.02.20.26346638 · Cited by 1 · see citing works
Meta-Evaluation Methodology and Benchmark for Automatic Story Generation
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- 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 · 2022: 1 · 2013: 1
- 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
- Social Sciences › Safety Research › 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 ›
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