提出轻量方法RN-F，高效识别大模型训练中的数据污染问题
RN-F: A Novel Approach for Mitigating Contaminated Data in Large Language Models
- 通过残差信号模式分析，无梯度、单次扫描检测数据污染
- 在多个大模型上检测准确率提升最高达10.5%
- 不依赖特定模型，适合所有大模型的数据质量评估
大语言模型已成为现代人工智能的基石，广泛应用于代码生成、虚拟助手、科研和企业自动化等领域。然而，测试数据与训练数据重叠（即数据污染）的问题严重威胁其应用可靠性。尽管该问题已受关注，现有方法仍难以有效识别或缓解污染。本文提出残差噪声指纹法（RN-F），一种无需梯度、单次遍历的检测框架，利用残差信号模式进行污染识别，不引入额外浮点运算。该方法轻量、模型无关且高效。我们在多个大模型和多种污染数据集上评估了RN-F，结果表明其持续优于现有最先进方法，在污染检测指标上最高提升10.5%。
原文摘要 · Abstract (English)
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.
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