🔗 Who cites «Proving Test Set Contamination in Black Box Language Models»

87 papers of our library cite it (of the 149,439 whose bibliographies we read) · 25 with a page here, the most cited first · ← back to «Proving Test Set Contamination in Black Box Language Models»

1. Investigating Data Contamination in Modern Benchmarks for Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.2024 · 137 citations
2. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
3. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and RecommendationsConference on Empirical Methods in Natural Language Processing (EMNLP)Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.2024 · 105 citations
4. Rethinking Benchmark and Contamination for Language Models with Rephrased SamplesarXiv:2311.04850Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.2023 · 103 citations
5. Task Contamination: Language Models May Not Be Few-Shot AnymoreProceedings of the AAAI Conference on Artificial IntelligenceChangmao Li, Jeffrey Flanigan2023 · 89 citations
6. Do Membership Inference Attacks Work on Large Language Models?arXiv:2402.07841Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.2024 · 87 citations
7. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
8. MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and TasksConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, et al.2024 · 74 citations
9. Benchmarking Benchmark Leakage in Large Language ModelsarXiv:2404.18824Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.2024 · 73 citations
10. On Memorization of Large Language Models in Logical ReasoningInternational Joint Conference on Natural Language Processing (IJCNLP)Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.2025 · 45 citations
11. “Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?Findings of the Association for Computational LinguisticsBeiduo Chen, Xinpeng Wang, Siyao Peng, et al.2024 · 18 citations
12. DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphConference on Neural Information Processing Systems (NeurIPS)Zhehao Zhang, Jiaao Chen, Diyi Yang2024 · 17 citations
13. Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle ChallengesInternational Conference on Computational Linguistics (COLING)Vinay Samuel, Yue Zhou, Henry Peng Zou2025 · 16 citations
14. Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern AnalysisConference of the European Chapter of the Association for Computational Linguistics (EACL)Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, et al.2026 · 2 citations
15. When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation EvaluationConference of the European Chapter of the Association for Computational Linguistics (EACL)David Tan, Pinzhen Chen, Josef van Genabith, et al.2026 · 2 citations
16. Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Zongyu Wu, Minhua Lin, Zhiwei Zhang, et al.2026 · 1 citation
17. MÖVE: A Holistic LLM Benchmark for the German Public SectorarXiv:2606.13111Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.2026 · 1 citation
18. Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection MethodsWorkshop on Generation, Evaluation and Metrics (GEM)Erfan Nourbakhsh, Mohammad Sadegh Sirjani, Amir Mousavi, et al.2026
19. Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.2026
20. Controllable Contamination Detection for Reliable LLM Evaluation with Statistical GuaranteesAnnual Meeting of the Association for Computational Linguistics (ACL)Zheng Zhang, Qi Liu, Siyuan Liang, et al.2026
21. JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM EvaluationFindings of the Association for Computational LinguisticsZhichao Shi, Xuhui Jiang, Chengjin Xu, et al.2026
22. Memory Dial: A Training Framework for Controllable Memorization in Language ModelsFindings of the Association for Computational LinguisticsXiangbo Zhang, Ali Emami2026
23. PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Jinhan Liu, Yibo Yang, Ruiying Lu, et al.2026
24. SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL BenchmarksAnnual Meeting of the Association for Computational Linguistics (ACL)Mohammadtaher Safarzadeh, Hitesh Laxmichand Patel, Afshin Oroojlooy, et al.2026
25. Thesis Proposal: Intentional Inference for Insight GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Kristýna Onderková2026

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