The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Data Portraits: Recording Foundation Model Training Data
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“Their solution is designed to facilitate lightweight membership inference within the training dataset.”
From Generative AI and intellectual property: a systematic literature review of possible issues and mitigations (2026) · cites this paper as Marone & Durme, 2023
In the citing paper
“Although MIAs are typically complex and expensive, Marone & Durme (2023) proposed using data portraits, i.e., artifacts that record training data and allow for downstream inspection of training datasets.”
From Generative AI and intellectual property: a systematic literature review of possible issues and mitigations (2026) · cites this paper as Marone & Durme, 2023
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Citations
Cites
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.301 · Cited by 693
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
Kim, B., Kim, H., & Kim, G. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1260 · Cited by 96
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus
Luccioni, A., & Viviano, J. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.18653/v1/2021.acl-short.24 · Cited by 69
Cited by
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
Sainz, O., Campos, J., García-Ferrero, I., Etxaniz, J., de Lacalle, O. L., & Agirre, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.722 · Cited by 129
Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.308 · Cited by 36
Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.10160 · Cited by 36
Towards collecting royalties for copyrighted data for generative models
Ludwig, H., Zhou, Y., Zawad, S., Ong, Y., Li, P., Butler, E., & Zahid, E. (2024) · 2024 IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00020 · Cited by 4
Arnaudo, A., Coppola, R., Morisio, M., Vetrò, A., Borghi, M., Raso, R., & Khan, B. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3928 · Cited by 2
Metadata
- Authors
- Marone, M., & Durme, B. V.
- Venue
- Advances in Neural Information Processing Systems 36
- Published
- 2023
- Pages
- 15121-15135
- Cited by
- 4
- References
- 4
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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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- CoChat. (2026). Citation check: Data Portraits: Recording Foundation Model Training Data. https://app.cochat.ai/papers/10.52202/075280-0665
- The paper
- Marone, M., & Durme, B. V. (2023). Data Portraits: Recording Foundation Model Training Data. Advances in Neural Information Processing Systems 36. https://doi.org/10.52202/075280-0665
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.
