Data Contamination in Language Models

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Data contamination occurs when language models encounter evaluation items or related information during pretraining or later training, potentially making benchmark scores reflect recall as well as generalization. Researchers detect it using exact and semantic overlap searches, membership-inference and likelihood tests, and behavioral comparisons on paraphrased, perturbed, or time-bounded benchmarks; some methods also probe hidden representations. Mitigation includes filtering or correcting training examples and designing dynamic, contamination-resistant evaluations. Detection remains unreliable when exposure is transformed or introduced during post-training, while aggressive filtering can remove useful data and dynamic tests cannot guarantee that information was unseen.

Latest papers

64 papers9 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
Oct 24, 2026Jian Yang, Peilin Liu, Xiaohan Li +4Clinical Language Model Evaluation LLM Auditing
The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China. · The College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
LLM Trojans that Increase Response Lengths and Cost
Oct 17, 2026Zhengxing Li, Guangmingmei Yang, Nader Alfares +2LLM Backdoor Attacks Adversarial Attacks on LLMs
Departments of Electrical Engineering and Computer Science & Engineering Penn State University University Park, PA 16802, USA
What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination
Oct 15, 2026Esther XinData Contamination in Language Models Benchmark Contamination
Independent Researcher
Lüshi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model
Oct 12, 2026Alex LiuLanguage Modeling Data Contamination in Language Models
Ybuild LLC
WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training
Oct 12, 2026Hengxiang Zhang, Junxian Liu, Hongxin WeiData Contamination in Language Models LLM Evaluation
Department of Statistics and Data Science, Southern University of Science and Technology
Wieszcz-XIX: A 3.1-Billion-Word Corpus of Pre-1918 Polish and Temporally Bounded Language Models Trained From Scratch
Oct 6, 2026Szymon KocurLanguage Modeling Language Model Pretraining
Independent Researcher, Poland.
No Hindsight for LLM Fact-Checkers: Measuring Leakage Channels in Misinformation Detection
Oct 4, 2026Kuan-Hua Wu Lu, Yohanes Andre SetiawanAutomated Fact-Checking Data Contamination in Language Models
Georgia Institute of Technology Atlanta, Georgia, USA
Misinformation Without Triggers: From Factual Answers to Downstream Decisions
Oct 2, 2026Lin Tian, Marian-Andrei RizoiuData Contamination in Language Models LLM Reliability
Behavioral Data Science, University of Technology Sydney
Correct, Don't Delete: Mitigating Emergent Misalignment with Corrective Supervision
Sep 29, 2026Jacob EpifanoLLM Alignment Emergent Misalignment in Language Models
Independent researcher
Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models
Sep 23, 2026Kaifeng Tan, Yudong Li, Linlin ShenLLM Evaluation Data Contamination in Language Models
Shenzhen University · Tsinghua University
Mirror, Mirror on the Wall: Prompt Echoing in Small Instruct Language Models
Sep 14, 2026Inez Okulska, Bartosz Naskręcki, Jan Piotrowski +1Data Contamination in Language Models Induction Heads
Centre for Credible AI, Warsaw University of Technology · Adam Mickiewicz University Poznan · Institute of Fundamental Technological Research, Polish Academy of Sciences
SynthSentry: Detecting Synthetic Data Contamination in Language Model Training Data
Sep 11, 2026Praveen Kumar Myakala, Ravichandra Namburi, Sowmya Keragodu Jayaramu +1AI-Generated Text Detection Training Data Curation
Independent AI Researcher Melissa, USA · Independent Researcher Frisco, USA · Independent Researcher Austin, USA
Molecular Déjà Vu: Digit-Level Retrieval of Molecular Properties in Frontier Language Models
Sep 4, 2026Matthias Busch, Marius Tacke, Sviatlana V. Lamaka +4LLM Evaluation LLM Reliability
Institute for Artificial Intelligence and Simulation in Mechanics, Hamburg University of Technology, Eißendorfer Straße, 21073 Hamburg, Germany · Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 21502 Geesthacht, Germany · Institute of Surface Science, Helmholtz-Zentrum Hereon, Max-Planck-Straße, 21502 Geesthacht, Germany +3
RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
Aug 30, 2026Xiyuan Zhou, Zhuoqi Li, Xinlei Wang +6Mathematical Reasoning Benchmarks LLM Mathematical Reasoning
Nanyang Technological University · The Chinese University of Hong Kong, Shenzhen · INSAIT, Sofia University “St. Kliment Ohridski” +2
CleanScore: Black-Box Benchmark Audits with Negative Controls and Sensitivity Bounds
Aug 27, 2026Jeffery Opoku, David BanaheneBenchmark Auditing Black-Box Model Auditing
School of Mathematical and Statistical Sciences The University of Texas Rio Grande Valley, Edinburg, Texas, USA · Department of Biostatistics, Robert Stempel College of Public Health and Social Work Florida International University, Miami, Florida, USA
Criticality in Dissimilar Decomposition and Undersampling of Random Datasets with Anomalies
Aug 14, 2026Ghurumuruhan GanesanData Contamination in Language Models
University of Bristol, UK
Excess Separability: Nuisance-Controlled Residual-Stream Probing for Benchmark Contamination Detection
Aug 12, 2026Florian BraunBenchmark Contamination Data Contamination in Language Models
Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection
Aug 11, 2026Zhen Yang, Mengqi Wang, Gengda Zhao +3Membership Inference Attacks Data Contamination in Language Models
The University of New South Wales
Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness
Aug 10, 2026Pinzhen Chen, Koel Dutta Chowdhury, Xiaoya Xu +20Data Contamination in Language Models Language Model Robustness
Queen’s University Belfast · University of Technology Nuremberg · Saarland University +12
Zero Gap Is Not Restoration: Stratified Per-Question Probability Evaluation and Step-wise Mitigation of Benchmark Contamination
Aug 7, 2026Ruijie Hou, Yueyang Jiao, Zhao Wang +1LLM Evaluation Memorization in Generative Models
Zhejiang University
Library Reachability in LSR-Synth: How Anti-Memorization Design Changes the Measurement of Symbolic Discovery
Jul 30, 2026Zhan'ao Yao, Liang Yin, Zhihao Gao +9Synthetic Benchmark Generation Data Contamination in Language Models
State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 1295 Dingxi Road, Shanghai 200050, China · Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China +4
How Context Attribution Handles What the Model Already Knows
Jul 26, 2026Quoc-Huy Trinh, Lin Zhu, Sebastian SzyllerLLM Evaluation LLM Interpretability
Aalto University
Novel Claim or Déjà Vu? Rethinking "Contamination-Free'' Dynamic Evaluation for Multimodal Automated Fact-Checking
Jul 26, 2026Haorui He, Xinwen Chen, Dacheng Wen +3Benchmark Contamination Automated Fact-Checking
Department of Interactive Media, Hong Kong Baptist University · School of Computing and Data Science, The University of Hong Kong · Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University
Spaghetti Architect: A Contamination-Resistant, By-Construction-Labelled, Multi-Language Code Dataset Generator
Jul 21, 2026Yuxiang JiSynthetic Benchmark Generation Data Contamination in Language Models
Sunway College
RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities
Jul 6, 2026Tarek Elsayed, Shiping Yang, Eunsong Koh +13Software Vulnerability Detection Data Contamination in Language Models
Simon Fraser University · Trinity University · Microsoft Research +1
Defending Against Harmful Supervision Hidden in Benign Samples
Jun 29, 2026Bang An, Yibo Yang, Dandan Guo +3Supervised Fine-Tuning Fine-Tuning
1King Abdullah University of Science and Technology · School of Artificial Intelligence, Jilin University
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models
Jun 29, 2026Shuaimin Li, Liyang Fan, Zeyang Li +9LLM Evaluation LLM Auditing
Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Shenzhen University · Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology +5
Uncertainty-based Debiasing and Unlearning for Decontamination
Jun 22, 2026Guangzhi Sun, Xiao Zhan, Mark GalesData Contamination in Language Models LLM Unlearning
Department of Engineering, University of Cambridge · VRAIN, Universitat Politècnica de València
Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software
Jun 18, 2026Arastoo Zibaeirad, Marco VieiraLLM Evaluation Software Vulnerability Detection
University of North Carolina at Charlotte, Charlotte, NC, USA
Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition
Jun 17, 2026Shiho Matta, Yin Jou Huang, Fei Cheng +3Language Model Pretraining Data Contamination in Language Models
Kyoto University · NII-LLMC

Oct 24, 2026J/K move · Enter open · S save

Auditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis

Jian Yang, Peilin Liu, Xiaohan Li, Songda Li+3

The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310052, China. · The College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.

Clinical Language Model Evaluation LLM Auditing Data Contamination in Language Models Rare Disease Diagnosis

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Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility remains unproven. A recent meta-analysis flagged augmented LLM diagnostic evaluations as having a high risk of bias due to suspected data leakage; however, the precise impact of these vulnerabilities on model inference has yet to be fully quantified. In this study, we present a comprehensive empirical audit to quantify evaluation artifacts across prevailing AI paradigms, using a repository of over 60,000 cases from diverse public and independent clinical cohorts. By reconstructing a state-of-the-art agentic framework, we demonstrate that candidate-space contamination artificially inflates performance: enforcing strict cross-dataset isolation resulted in a marked performance decline, with the cross-dataset mean Recall@1 decreasing from 54.3% (under conventional one-vs-rest evaluation) to 40.3% (Hedges' g = 0.94). Similar spatial overfitting in supervised classifiers caused diagnostic accuracy to drop from an internal mean of 42.9% to 16.7% across external cohorts (Cohen's d = 2.01). Furthermore, we quantify how LLMs infer diagnoses by exploiting retrospective therapeutic cues in clinical narratives, and how temporal misalignment in foundational knowledge bases leads to performance fluctuations. We conclude that prevailing evaluation protocols for augmented LLM systems inadvertently reflect data leakage rather than genuine medical reasoning. To address these systematic artifacts, we propose a rigorous evaluation framework - coupling strict data-isolation protocols with standardized benchmarking safeguards - to ensure future diagnostic AI for rare diseases is evaluated on actual clinical robustness.