Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation

Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray


Abstract
In the era of evaluating large language models (LLMs), data contamination has become an increasingly prominent concern. To address this risk, LLM benchmarking has evolved from a static to a dynamic paradigm. In this work, we conduct an in-depth analysis of existing static and dynamic benchmarks for evaluating LLMs. We first examine methods that enhance static benchmarks and identify their inherent limitations. We then highlight a critical gap—the lack of standardized criteria for evaluating dynamic benchmarks. Based on this observation, we propose a series of optimal design principles for dynamic benchmarking and analyze the limitations of existing dynamic benchmarks.This survey provides a concise yet comprehensive overview of recent advancements in data contamination research, offering valuable insights and a clear guide for future research efforts. We maintain a GitHub repository to continuously collect both static and dynamic benchmarking methods for LLMs. The repository can be found at this link.
Anthology ID:
2025.emnlp-main.511
Volume:
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10080–10098
Language:
URL:
https://preview.aclanthology.org/automate-pypi-publishing/2025.emnlp-main.511/
DOI:
10.18653/v1/2025.emnlp-main.511
Bibkey:
Cite (ACL):
Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, and Baishakhi Ray. 2025. Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 10080–10098, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (Chen et al., EMNLP 2025)
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PDF:
https://preview.aclanthology.org/automate-pypi-publishing/2025.emnlp-main.511.pdf
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