--- title: "Who cites Benchmark Data Contamination — 65 citing papers" description: "65 papers of the Elamur Research library cite «Benchmark Data Contamination of Large Language Models: A Survey», found in their…" canonical: "https://research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Benchmark Data Contamination of Large Language…](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/) / cited by # Who cites «Benchmark Data Contamination of Large Language Models: A Survey» 65 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Benchmark Data Contamination of Large Language Models: A Survey»](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/) 1. [**FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI arXiv:2411.04872**Elliot Glazer, Ege Erdil, Tamay Besiroglu, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/glazer-2024-frontiermath-benchmark-evaluating-advanced-mathematical/) 2. **[TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection](https://md.research.elamur.ai/free/en/papers/xu-2025-triplefact-defending-data-contamination-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Xu, Nan Yan*2025 · 12 citations* 3. **[MAVEN-FACT: A Large-scale Event Factuality Detection Dataset](https://md.research.elamur.ai/free/en/papers/li-2024-maven-fact-large-scale-event/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunyang Li, Hao Peng, Xiaozhi Wang, et al.*2024 · 6 citations* 4. **[Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network](https://md.research.elamur.ai/free/en/papers/fang-2024-advancing-arabic-sentiment-analysis-arsen/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Yang Fang, Cheng Xu, Shuhao Guan, et al.*2024 · 4 citations* 5. **[LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-llmeval-fair-large-scale-longitudinal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ming Zhang, Yujiong Shen, Jingyi Deng, et al.*2026 · 3 citations* 6. **[LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection](https://md.research.elamur.ai/free/en/papers/xu-2026-livefact-dynamic-time-aware-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Xu, Changhong Jin, Yingjie Niu, et al.*2026 · 2 citations* 7. **[A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models](https://md.research.elamur.ai/free/en/papers/petkar-2026-graph-talks-but-who-s/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Soham Petkar, Hari Aakash K, Anirudh Vempati, et al.*2026 · 1 citation* 8. **[Test of Time: Rethinking Temporal Signal of Benchmark Contamination](https://md.research.elamur.ai/free/en/papers/zhang-2026-test-time-rethinking-temporal-signal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Terry Jingchen Zhang, Gopal Dev, Ning Wang, et al.*2026 · 1 citation* 9. **[Benchmark Data Contamination in Underrepresented Languages: A Comprehensive Analysis Using Brazilian Data](https://md.research.elamur.ai/free/en/papers/vilar-2026-benchmark-data-contamination-underrepresented-languages/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Iriedson Souto Maior de Moraes Vilar, David Candeia Maia, João Brunet, et al.*2026* 10. **[BenchNavigator: A Discovery Interface for Comparing LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/sokol-2026-benchnavigator-discovery-interface-comparing-llm/) [Workshop on Evaluating Evaluations (EvalEval)](https://md.research.elamur.ai/free/en/venues/evaleval/)**Anna Sokol, Inge Vejsbjerg, Elizabeth M. Daly, et al.*2026* 11. [**Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian arXiv:2605.19711**Yun Hao, Reihaneh Amooie, Wietse de Vries, et al.*2026*](https://md.research.elamur.ai/free/en/papers/hao-2026-large-language-models-reliably-correct/) 12. **[Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection Prompting](https://md.research.elamur.ai/free/en/papers/yin-2026-enhancing-zero-shot-time-series/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyou Yin, Ceyao Zhang, Min Hu, et al.*2026* 13. **[Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2026-inflated-excellence-or-true-performance/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangxu Zhang, Lei Li, Yanyun Zhou, et al.*2026* 14. **[Thesis Proposal: Intentional Inference for Insight Generation](https://md.research.elamur.ai/free/en/papers/onderkova-2026-thesis-proposal-intentional-inference-insight/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kristýna Onderková*2026* 51 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/) — Benchmark Data Contamination of Large Language Models: A Survey