# Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges

arXiv: https://arxiv.org/abs/2409.09927 · HTML: https://mlsanity.com/paper/2409.09927/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://mlsanity.com/paper/2310.18018/) (2023)
- [Proving Test Set Contamination in Black Box Language Models](https://mlsanity.com/paper/2310.17623/) (2023)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks](https://mlsanity.com/paper/2305.10160/) (2023)
- [Data Contamination Can Cross Language Barriers](https://mlsanity.com/paper/2406.13236/) (2024)
- [ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2404.15592/) (2024)

## Built on by (most cited first, 18 of 18)

- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [On The Fragility of Benchmark Contamination Detection in Reasoning Models](https://mlsanity.com/paper/2510.02386/) (2025)
- [The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective](https://mlsanity.com/paper/2412.09460/) (2024)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [None of the Others: a General Technique to Distinguish Reasoning from Memorization in Multiple-Choice LLM Evaluation Benchmarks](https://mlsanity.com/paper/2502.12896/) (2025)
- [NotSoTiny: A Large, Living Benchmark for RTL Code Generation](https://mlsanity.com/paper/2512.20823/) (2025)
- [ClaimDB: A Fact Verification Benchmark over Large Structured Data](https://mlsanity.com/paper/2601.14698/) (2026)
- [Simulating Training Data Leakage in Multiple-Choice Benchmarks for LLM Evaluation](https://mlsanity.com/paper/2505.24263/) (2025)
- [LLM Benchmark Datasets Should Be Contamination-Resistant](https://mlsanity.com/paper/2605.19999/) (2026)
- [LLM Performance for Code Generation on Noisy Tasks](https://mlsanity.com/paper/2505.23598/) (2025)
- [Text-to-SQL Task-oriented Dialogue Ontology Construction](https://mlsanity.com/paper/2507.23358/) (2025)
- [Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora](https://mlsanity.com/paper/2601.14994/) (2026)
- [Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation](https://mlsanity.com/paper/2603.23517/) (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)
- [Beyond Visual Understanding: Introducing PARROT-360V for Vision Language Model Benchmarking](https://mlsanity.com/paper/2411.15201/) (2024)
- [RN-F: A Novel Approach for Mitigating Contaminated Data in Large Language Models](https://mlsanity.com/paper/2505.13249/) (2025)
- [Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification](https://mlsanity.com/paper/2603.20965/) (2026)
- [When Is Benchmark Contamination Detectable? Information Limits and Power-Calibrated Audits](https://mlsanity.com/paper/2608.07914/) (2026)

## Abstract (arXiv)

As large language models achieve increasingly impressive results, questions arise about whether such performance is from generalizability or mere data memorization. Thus, numerous data contamination detection methods have been proposed. However, these approaches are often validated with traditional benchmarks and early-stage LLMs, leaving uncertainty about their effectiveness when evaluating state-of-the-art LLMs on the contamination of more challenging benchmarks. To address this gap and provide a dual investigation of SOTA LLM contamination status and detection method robustness, we evaluate five contamination detection approaches with four state-of-the-art LLMs across eight challenging datasets often used in modern LLM evaluation. Our analysis reveals that (1) Current methods have non-trivial limitations in their assumptions and practical applications; (2) Notable difficulties exist in detecting contamination introduced during instruction fine-tuning with answer augmentation; and (3) Limited consistencies between SOTA contamination detection techniques. These findings highlight the complexity of contamination detection in advanced LLMs and the urgent need for further research on robust and generalizable contamination evaluation. Our code is available at https://github.com/vsamuel2003/data-contamination.