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NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
Open accessAbstract
In this position paper, we argue that the classical evaluation on Natural Language Processing (NLP) tasks using annotated benchmarks is in trouble.The worst kind of data contamination happens when a Large Language Model (LLM) is trained on the test split of a benchmark, and then evaluated in the same benchmark.The extent of the problem is unknown, as it is not straightforward to measure.Contamination causes an overestimation of the performance of a contaminated model in a target benchmark and associated task with respect to their noncontaminated counterparts.The consequences can be very harmful, with wrong scientific conclusions being published while other correct ones are discarded.This position paper defines different levels of data contamination and argues for a community effort, including the development of automatic and semi-automatic measures to detect when data from a benchmark was exposed to a model, and suggestions for flagging papers with conclusions that are compromised by data contamination.
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“Because data contamination is a well known issue in the field (Balloccu et al. [2024], Magar and Schwartz [2022], Sainz et al. [2023], Jacovi et al. [2023], Xu et al. [2024])”
From A Careful Examination of Large Language Model Performance on Grade School Arithmetic (2024) · cites this paper as Sainz et al. [2023]
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Metadata
- Authors
- Sainz, O., Campos, J., García-Ferrero, I., Etxaniz, J., de Lacalle, O. L., & Agirre, E.
- Affiliations
- University of the Basque Country (ES)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Published
- 2023
- Pages
- 10776-10787
- OpenAlex
- W4389518953
- Cited by
- 129
- References
- 16
- Citations by year
- 2026: 57 · 2025: 45 · 2024: 27
- FWCI
- 20.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Eusko Jaurlaritza
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- contaminated data, benchmark contamination, large language models, contamination detection, NLP evaluation
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