@inproceedings{nourbakhsh-etal-2026-llm,
title = "Are {LLM} Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods",
author = "Nourbakhsh, Erfan and
Sirjani, Mohammad Sadegh and
Mousavi, Amir and
Nguyen, Khoa and
Quarles, John and
Xie, Mimi and
Slavin, Rocky",
editor = "Mille, Simon and
Gehrmann, Sebastian and
Schmidtov{\'a}, Patr{\'i}cia and
Du{\v{s}}ek, Ond{\v{r}}ej and
Fadaee, Marzieh and
Lo, Kyle and
Santus, Enrico and
Stanovsky, Gabriel",
booktitle = "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics ({GEM})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gem-main.50/",
pages = "518--539",
ISBN = "979-8-89176-423-1",
abstract = "Large Language Models (LLMs) are trained on web-scale corpora, increasing the risk that benchmark test data appears in training sets and inflates reported performance. We present a systematic literature review of 55 studies on LLM benchmark contamination through late 2025. Our contributions are: (1) a four-tier contamination taxonomy (Exact, Syntactic, Semantic, Task-Level; T1{--}T4); (2) a comparative analysis of five detection families (string-matching, likelihood-based, membership inference, LLM-prompted detection, and benchmark auditing), including access assumptions and failure modes; (3) a synthesis of contamination evidence on MMLU, GSM8K, HumanEval, and HellaSwag by measurement construct; (4) a comparative evaluation of mitigation strategies across lifecycle points, access assumptions, and evidence maturity; and (5) a Contamination Transparency Card (CTC) framework for future releases. Across studies, no detection method is consistently reliable across contamination tiers, model-access settings, and training stages. We identify instruction tuning as a persistent blind spot, note that RL/post-training contamination auditing is only beginning to mature, and report inflation estimates spanning roughly 6{\%}{--}40{\%} under benchmark- and setting-dependent assumptions."
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%0 Conference Proceedings
%T Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods
%A Nourbakhsh, Erfan
%A Sirjani, Mohammad Sadegh
%A Mousavi, Amir
%A Nguyen, Khoa
%A Quarles, John
%A Xie, Mimi
%A Slavin, Rocky
%Y Mille, Simon
%Y Gehrmann, Sebastian
%Y Schmidtová, Patrícia
%Y Dušek, Ondřej
%Y Fadaee, Marzieh
%Y Lo, Kyle
%Y Santus, Enrico
%Y Stanovsky, Gabriel
%S Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-423-1
%F nourbakhsh-etal-2026-llm
%X Large Language Models (LLMs) are trained on web-scale corpora, increasing the risk that benchmark test data appears in training sets and inflates reported performance. We present a systematic literature review of 55 studies on LLM benchmark contamination through late 2025. Our contributions are: (1) a four-tier contamination taxonomy (Exact, Syntactic, Semantic, Task-Level; T1–T4); (2) a comparative analysis of five detection families (string-matching, likelihood-based, membership inference, LLM-prompted detection, and benchmark auditing), including access assumptions and failure modes; (3) a synthesis of contamination evidence on MMLU, GSM8K, HumanEval, and HellaSwag by measurement construct; (4) a comparative evaluation of mitigation strategies across lifecycle points, access assumptions, and evidence maturity; and (5) a Contamination Transparency Card (CTC) framework for future releases. Across studies, no detection method is consistently reliable across contamination tiers, model-access settings, and training stages. We identify instruction tuning as a persistent blind spot, note that RL/post-training contamination auditing is only beginning to mature, and report inflation estimates spanning roughly 6%–40% under benchmark- and setting-dependent assumptions.
%U https://aclanthology.org/2026.gem-main.50/
%P 518-539
Markdown (Informal)
[Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods](https://aclanthology.org/2026.gem-main.50/) (Nourbakhsh et al., GEM 2026)
ACL
- Erfan Nourbakhsh, Mohammad Sadegh Sirjani, Amir Mousavi, Khoa Nguyen, John Quarles, Mimi Xie, and Rocky Slavin. 2026. Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods. In Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM), pages 518–539, San Diego, California, USA. Association for Computational Linguistics.