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RN-F: A Novel Approach for Mitigating Contaminated Data in Large Language Models

arXiv 2505.13249 ↗First posted 19 May 2025cs.LGcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) have become foundational in modern artificial intelligence, powering a wide range of applications from code generation and virtual assistants to scientific research and enterprise automation. However, concerns about data contamination--where test data overlaps with training data--have raised serious questions about the reliability of these applications. Despite awareness of this issue, existing methods fall short in effectively identifying or mitigating contamination. In this paper, we propose Residual-Noise Fingerprinting (RN-F), a novel framework for detecting contaminated data in LLMs. RN-F is a single-pass, gradient-free detection method that leverages residual signal patterns without introducing additional floating-point operations. Our approach is lightweight, model-agnostic, and efficient. We evaluate RN-F on multiple LLMs across various contaminated datasets and show that it consistently outperforms existing state-of-the-art methods, achieving performance improvements of up to 10.5% in contamination detection metrics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 19 May 2025.

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References · 20

  1. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
  3. 2023TinyStories: How Small Can Language Models Be and Still Speak Coherent English?cited by 449
  4. 2023Time Travel in LLMs: Tracing Data Contamination in Large Language Modelscited by 177
  5. 2023Investigating Data Contamination in Modern Benchmarks for Large Language Modelscited by 138
  6. 2024Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Modelscited by 122
  7. 2022Diffusion Probabilistic Model Made Slimcited by 121
  8. 2023Task Contamination: Language Models May Not Be Few-Shot Anymorecited by 99
  9. 2024ReCaLL: Membership Inference via Relative Conditional Log-Likelihoodscited by 64
  10. 2024Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Methodcited by 55
  11. 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoffcited by 51
  12. 2024ConStat: Performance-Based Contamination Detection in Large Language Modelscited by 33
8 more
  1. 2024TabPFGen -- Tabular Data Generation with TabPFNcited by 28
  2. 2021M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretrainingcited by 26
  3. 2024Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptionscited by 26
  4. 2024Probing Language Models for Pre-training Data Detectioncited by 25
  5. 2024Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challengescited by 18
  6. 2024Rethinking Backdoor Detection Evaluation for Language Modelscited by 9
  7. 2024ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinementcited by 3
  8. 2025Membership Inference Risks in Quantized Models: A Theoretical and Empirical Studycited by 3

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