# A Survey on Data Contamination for Large Language Models

arXiv: https://arxiv.org/abs/2502.14425 · HTML: https://mlsanity.com/paper/2502.14425/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 60 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 30 of 45)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://mlsanity.com/paper/2210.01504/) (2022)
- [Who's Harry Potter? Approximate Unlearning in LLMs](https://mlsanity.com/paper/2310.02238/) (2023)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [Will we run out of data? Limits of LLM scaling based on human-generated data](https://mlsanity.com/paper/2211.04325/) (2022)
- [Rethinking Benchmark and Contamination for Language Models with Rephrased Samples](https://mlsanity.com/paper/2311.04850/) (2023)
- [A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://mlsanity.com/paper/2405.00332/) (2024)
- [Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://mlsanity.com/paper/2306.04181/) (2023)
- [Platypus: Quick, Cheap, and Powerful Refinement of LLMs](https://mlsanity.com/paper/2308.07317/) (2023)
- [Benchmark Data Contamination of Large Language Models: A Survey](https://mlsanity.com/paper/2406.04244/) (2024)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Preference Leakage: A Contamination Problem in LLM-as-a-judge](https://mlsanity.com/paper/2502.01534/) (2025)
- [DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks](https://mlsanity.com/paper/2309.17167/) (2023)
- [Task Contamination: Language Models May Not Be Few-Shot Anymore](https://mlsanity.com/paper/2312.16337/) (2023)
- [Rethinking LLM Memorization through the Lens of Adversarial Compression](https://mlsanity.com/paper/2404.15146/) (2024)
- [Investigating Data Contamination for Pre-training Language Models](https://mlsanity.com/paper/2401.06059/) (2024)
- [Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap](https://mlsanity.com/paper/2402.19450/) (2024)
- [Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation](https://mlsanity.com/paper/2402.11443/) (2024)
- [DE-COP: Detecting Copyrighted Content in Language Models Training Data](https://mlsanity.com/paper/2402.09910/) (2024)
- [EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories](https://mlsanity.com/paper/2404.00599/) (2024)
- [Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation](https://mlsanity.com/paper/2102.01818/) (2021)
- [Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models](https://mlsanity.com/paper/2403.04811/) (2024)
- [LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction](https://mlsanity.com/paper/2312.12343/) (2023)
- [Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method](https://mlsanity.com/paper/2409.14781/) (2024)
- [UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI](https://mlsanity.com/paper/2407.00106/) (2024)
- [Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation](https://mlsanity.com/paper/2309.10677/) (2023)

## Built on by (most cited first, 30 of 35)

- [SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?](https://mlsanity.com/paper/2509.16941/) (2025)
- [Winning Gold at IMO 2025 with a Model-Agnostic Verification-and-Refinement Pipeline](https://mlsanity.com/paper/2507.15855/) (2025)
- [Search Arena: Analyzing Search-Augmented LLMs](https://mlsanity.com/paper/2506.05334/) (2025)
- [LastingBench: Defend Benchmarks Against Knowledge Leakage](https://mlsanity.com/paper/2506.21614/) (2025)
- [Confidence in Large Language Model Evaluation: A Bayesian Approach to Limited-Sample Challenges](https://mlsanity.com/paper/2504.21303/) (2025)
- [A validity-guided workflow for robust large language model research in psychology](https://mlsanity.com/paper/2507.04491/) (2025)
- [BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models](https://mlsanity.com/paper/2509.24210/) (2025)
- [ClawEnvKit: Automatic Environment Generation for Claw-Like Agents](https://mlsanity.com/paper/2604.18543/) (2026)
- [The Impact of Post-training on Data Contamination](https://mlsanity.com/paper/2601.06103/) (2026)
- [Where Did It Go Wrong? Attributing Undesirable LLM Behaviors via Representation Gradient Tracing](https://mlsanity.com/paper/2510.02334/) (2025)
- [MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis](https://mlsanity.com/paper/2509.11335/) (2025)
- [When Large Language Models Know the Table: A Framework for Assessing Data Contamination in Tabular Datasets](https://mlsanity.com/paper/2510.20351/) (2025)
- [In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts](https://mlsanity.com/paper/2603.25857/) (2026)
- [The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation](https://mlsanity.com/paper/2605.21856/) (2026)
- [DynaSolidGeo: A Dynamic Benchmark for Genuine Spatial Mathematical Reasoning of VLMs in Solid Geometry](https://mlsanity.com/paper/2510.22340/) (2025)
- [DVD: A Robust Method for Detecting Variant Contamination in Large Language Model Evaluation](https://mlsanity.com/paper/2601.04895/) (2026)
- [Measuring AI Reasoning: A Guide for Researchers](https://mlsanity.com/paper/2605.02442/) (2026)
- [TEMPO: Temporal Enforcement via Mode-Separated Policy Optimization for Trustworthy LLM Backtesting](https://mlsanity.com/paper/2605.18843/) (2026)
- [TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMs](https://mlsanity.com/paper/2605.24079/) (2026)
- [Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications](https://mlsanity.com/paper/2605.26133/) (2026)
- [How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location](https://mlsanity.com/paper/2602.08548/) (2026)
- [When AI Navigates the Fog of War](https://mlsanity.com/paper/2603.16642/) (2026)
- [DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models](https://mlsanity.com/paper/2603.23514/) (2026)
- [RoMathExam: A Longitudinal Dataset of Romanian Math Exams (1895-2025) with a Seven-Decade Core (1957-2025)](https://mlsanity.com/paper/2604.16392/) (2026)
- [Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models](https://mlsanity.com/paper/2605.18824/) (2026)
- [Provable Joint Decontamination for Benchmarking Multiple Large Language Models](https://mlsanity.com/paper/2605.21543/) (2026)
- [Rethinking Molecular Text Representations for LLMs: An Empirical Study](https://mlsanity.com/paper/2606.03057/) (2026)
- [Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering](https://mlsanity.com/paper/2606.20950/) (2026)
- [To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation](https://mlsanity.com/paper/2606.29481/) (2026)
- [EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures](https://mlsanity.com/paper/2606.30219/) (2026)

## Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have demonstrated significant progress in various areas, such as text generation and code synthesis. However, the reliability of performance evaluation has come under scrutiny due to data contamination-the unintended overlap between training and test datasets. This overlap has the potential to artificially inflate model performance, as LLMs are typically trained on extensive datasets scraped from publicly available sources. These datasets often inadvertently overlap with the benchmarks used for evaluation, leading to an overestimation of the models' true generalization capabilities. In this paper, we first examine the definition and impacts of data contamination. Secondly, we review methods for contamination-free evaluation, focusing on three strategies: data updating-based methods, data rewriting-based methods, and prevention-based methods. Specifically, we highlight dynamic benchmarks and LLM-driven evaluation methods. Finally, we categorize contamination detecting methods based on model information dependency: white-Box, gray-Box, and black-Box detection approaches. Our survey highlights the requirements for more rigorous evaluation protocols and proposes future directions for addressing data contamination challenges.