mlsanityData to 7 Oct 2026

A Comprehensive Survey of Contamination Detection Methods in Large Language Models

arXiv 2404.00699 ↗First posted 31 Mar 2024cs.CLcited by 12

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Abstract (arXiv)

With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical. Business applications and fundraising in Artificial Intelligence (AI) have reached a scale at which a few percentage points gained on popular question-answering benchmarks could translate into dozens of millions of dollars, placing high pressure on model integrity. At the same time, it is becoming harder and harder to keep track of the data that LLMs have seen; if not impossible with closed-source models like GPT-4 and Claude-3 not divulging any information on the training set. As a result, contamination becomes a major issue: LLMs' performance may not be reliable anymore, as the high performance may be at least partly due to their previous exposure to the data. This limitation jeopardizes real capability improvement in the field of NLP, yet, there remains a lack of methods on how to efficiently detect contamination. In this paper, we survey all recent work on contamination detection with LLMs, analyzing their methodologies and use cases to shed light on the appropriate usage of contamination detection methods. Our work calls the NLP research community's attention into systematically taking into account contamination bias in LLM evaluation.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 103 of its 152 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

103 of 152 references.

References · 103

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38 more
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50 most cited of 103 All 103 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 5.

12 papers.

  1. 2025STEER-ME: Assessing the Microeconomic Reasoning of Large Language Modelscited by 7
  2. 2025Economic Evaluation of LLMscited by 4
  3. 2024Presence or Absence: Are Unknown Word Usages in Dictionaries?cited by 3
  4. 2024Enriching Datasets with Demographics through Large Language Models: What's in a Name?cited by 3
  5. 2025Neutralizing Bias in LLM Reasoning using Entailment Graphscited by 3
  6. 2024Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systemscited by 2
  7. 2025LLM Performance for Code Generation on Noisy Taskscited by 1
  8. 2026TRACER: A Semantic-Aware Framework for Fine-Grained Contamination Detection in Code LLMscited by 1
  9. 2026Representational Depth of Evaluation Awareness Shifts With Scale in Open-Weight Language Modelscited by 1
  10. 2026Physics-R1: An Audited Olympiad Corpus and Released Verifiers for Visual Physics Reasoningcited by 0
  11. 2026Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scorescited by 0
  12. 2026Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot Seecited by 0