Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks
Open accessAbstract
Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora.For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination.Strategies such as leaderboards with hidden answers, or using test data which is guaranteed to be unseen, are expensive and become fragile with time.Assuming that all relevant actors value clean test data and will cooperate to mitigate data contamination, what can be done?We propose three strategies that can make a difference: (1) Test data made public should be encrypted with a public key and licensed to disallow derivative distribution; (2) demand training exclusion controls from closed API holders, and protect your test data by refusing to evaluate without them; (3) avoid data which appears with its solution on the internet, and release the web-page context of internet-derived data along with the data.These strategies are practical and can be effective in preventing data contamination.
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Encrypting the test set prior to uploading can enhance security.
“As suggested by Jacovi et al. (2023), encrypting the test set prior to uploading can enhance security.”
From Benchmarking Benchmark Leakage in Large Language Models (2024) · cites this paper as Jacovi et al. (2023)
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
LLaMA: 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 · see citing works
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710 · see citing works
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 · see citing works
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · see citing works
TinyBERT: Distilling BERT for natural language understanding
Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L., Wang, F., & Liu, Q. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.372 · Cited by 1,786 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
Extracting Training Data from Diffusion ModelsPreprint
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., & Wallace, E. (2023) · USENIX Security Symposium · doi:10.48550/arxiv.2301.13188 · Cited by 1,112 · see citing works
Beyond accuracy: behavioral testing of NLP models with CheckList
Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.442 · Cited by 890 · see citing works
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
Johnson, R., & Zhang, T. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1011 · Cited by 787 · see citing works
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction ModelPreprint
Gao, P., Han, J., Zhang, R., Lin, Z.-Y., Geng, S., Zhou, A., Zhang, W., Lu, P., He, C.-H., Yue, X., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2304.15010 · Cited by 777 · see citing works
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works
Felbo, B., Mislove, A. E., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 712 · see citing works
Algorithms for approximate string matching
Ukkonen, E. (1985) · Information and Control · doi:10.1016/s0019-9958(85)80046-2 · Cited by 610 · see citing works
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569 · see citing works
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.85 · Cited by 519 · see citing works
Holistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480 · see citing works
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Fabbri, A., Li, I., She, T., Li, S., & Radev, D. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1102 · Cited by 449 · see citing works
Robust fine-tuning of zero-shot models
Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00780 · Cited by 428 · see citing works
Datasets: A Community Library for Natural Language Processing
Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2021.emnlp-demo.21 · Cited by 343 · see citing works
Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.507 · Cited by 316 · see citing works
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.05482 · Cited by 307 · see citing works
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.98 · Cited by 306 · see citing works
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Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022) · arXiv · doi:10.48550/arxiv.2202.07646 · Cited by 204 · see citing works
A Watermark for Large Language Models
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023) · arXiv · doi:10.48550/arxiv.2301.10226 · Cited by 194 · see citing works
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Ganesh, P., Chen, Y., Lou, X., Khan, M. A., Yang, Y., Sajjad, H., Nakov, P., Chen, D., & Winslett, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00413 · Cited by 190 · see citing works
Can AI-Generated Text be Reliably Detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11156 · Cited by 161 · see citing works
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTPreprint
Wei, X., Cui, X., Cheng, N., Wang, X., Zhang, X., Huang, S., Xie, P., Xu, J., Chen, Y., Zhang, M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.10205 · Cited by 157 · see citing works
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Liu, N., Zhang, T., & Liang, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.467 · Cited by 143 · see citing works
Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4
Chang, K. K., Cramer, M. H., Soni, S., & Bamman, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.453 · Cited by 110 · see citing works
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Razeghi, Y., Logan IV, R. L., Gardner, M., & Singh, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.59 · Cited by 89 · see citing works
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A fast bit-vector algorithm for approximate string matching based on dynamic programming
Myers, E. W. (1998) · Lecture notes in computer science · doi:10.1007/bfb0030777 · Cited by 71 · see citing works
Contrastive Distillation on Intermediate Representations for Language Model Compression
Sun, S., Gan, Z., Fang, Y., Cheng, Y., Wang, S., & Liu, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.36 · Cited by 62 · see citing works
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Rajeev, P. V., & Rekha, V. S. (2015) · 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] · doi:10.1109/iccpct.2015.7159433 · Cited by 46 · see citing works
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation
Khashabi, D., Stanovsky, G., Bragg, J., Lourie, N., Kasai, J., Choi, Y., Smith, N. A., & Weld, D. S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.787 · Cited by 38 · see citing works
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Piktus, A., Akiki, C., Villegas, P., Laurençon, H., Dupont, G., Luccioni, A. S., Jernite, Y., & Rogers, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.29 · Cited by 16 · see citing works
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Choshen, L., Venezian, E., Slonim, N., & Katz, Y. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.03044 · Cited by 13 · see citing works
Coreference in Wikipedia: Main Concept Resolution
Ghaddar, A., & Langlais, P. (2016) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1023 · Cited by 12 · see citing works
Data Portraits: Recording Foundation Model Training Data
Marone, M., & Durme, B. V. (2023) · neural information processing systems · doi:10.52202/075280-0665 · Cited by 10 · see citing works
ThoughtSource: A central hub for large language model reasoning data
Ott, S., Hebenstreit, K., Liévin, V., Hother, C. E., Moradi, M., Mayrhauser, M., Praas, R., Winther, O., & Samwald, M. (2023) · arXiv · doi:10.48550/arxiv.2301.11596
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Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works
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Zakka, C., Shad, R., Chaurasia, A., Dalal, A. R., Kim, J. L., Moor, M., Fong, R., Phillips, C., Alexander, K., Ashley, E., et al. (2024) · NEJM AI · doi:10.1056/aioa2300068 · Cited by 424 · see citing works
Levy, M., Jacoby, A., & Goldberg, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.818 · Cited by 119 · see citing works
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- Authors
- Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y.
- Affiliations
- Bar-Ilan University (IL)
- Google (United States) (US)
- Corresponding author
- Jacovi, Alon
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 5075-5084
- OpenAlex
- W4389518805
- Cited by
- 36
- References
- 43
- Citations by year
- 2026: 11 · 2025: 6 · 2024: 12 · 2023: 7
- FWCI
- 6.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nd)
- arXiv (Cornell University)
- Funders
- European Commission (HORIZON2020)
- H2020 European Research Council (802774)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- contaminated data, evaluation benchmark, training data leakage, public-key encryption, data provenance, LLM evaluation
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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