A Survey on Data Contamination for Large Language Models

• URL: http://arxiv.org/abs/2502.14425v1
• Date: Thu, 20 Feb 2025 10:23:27 GMT
• Title: A Survey on Data Contamination for Large Language Models
• Authors: Yuxing Cheng, Yi Chang, Yuan Wu,
• Abstract summary: Large Language Models (LLMs) have demonstrated significant progress in various areas, such as text generation and code synthesis. The reliability of performance evaluation has come under scrutiny due to data contamination.
• Score: 12.431575579432458
• License:
• Abstract: 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.

Related papers

• Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions [20.51842378080194]
  Large language models (LLMs) have demonstrated great performance across various benchmarks, showing potential as general-purpose task solvers. A significant concern in their evaluation is data contamination, where overlap between training data and evaluation datasets inflates performance assessments. We systematically review 47 papers on data contamination detection, categorize the underlying assumptions, and assess whether they have been rigorously validated.
  arXiv  Detail & Related papers  (2024-10-24T17:58:22Z)
• Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method [108.56493934296687]
  We introduce a divergence-based calibration method, inspired by the divergence-from-randomness concept, to calibrate token probabilities for pretraining data detection. We have developed a Chinese-language benchmark, PatentMIA, to assess the performance of detection approaches for LLMs on Chinese text.
  arXiv  Detail & Related papers  (2024-09-23T07:55:35Z)
• Training on the Benchmark Is Not All You Need [52.01920740114261]
  We propose a simple and effective data leakage detection method based on the contents of multiple-choice options. Our method is able to work under black-box conditions without access to model training data or weights. We evaluate the degree of data leakage of 31 mainstream open-source LLMs on four benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T11:09:44Z)
• Unlearnable Examples Detection via Iterative Filtering [84.59070204221366]
  Deep neural networks are proven to be vulnerable to data poisoning attacks. It is quite beneficial and challenging to detect poisoned samples from a mixed dataset. We propose an Iterative Filtering approach for UEs identification.
  arXiv  Detail & Related papers  (2024-08-15T13:26:13Z)
• PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models [41.772263447213234]
  Large language models (LLMs) are known to be trained on vast amounts of data, which may unintentionally or intentionally include data from commonly used benchmarks. This inclusion can lead to cheatingly high scores on model leaderboards, yet result in disappointing performance in real-world applications. We introduce PaCoST, a Paired Confidence Significance Testing to effectively detect benchmark contamination in LLMs.
  arXiv  Detail & Related papers  (2024-06-26T13:12:40Z)
• Probing Language Models for Pre-training Data Detection [11.37731401086372]
  We propose to utilize the probing technique for pre-training data detection by examining the model's internal activations. Our method is simple and effective and leads to more trustworthy pre-training data detection.
  arXiv  Detail & Related papers  (2024-06-03T13:58:04Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models [53.84677081899392]
  KIEval is a Knowledge-grounded Interactive Evaluation framework for large language models. It incorporates an LLM-powered "interactor" role for the first time to accomplish a dynamic contamination-resilient evaluation. Extensive experiments on seven leading LLMs across five datasets validate KIEval's effectiveness and generalization.
  arXiv  Detail & Related papers  (2024-02-23T01:30:39Z)
• Rethinking Benchmark and Contamination for Language Models with Rephrased Samples [49.18977581962162]
  Large language models are increasingly trained on all the data ever produced by humans. Many have raised concerns about the trustworthiness of public benchmarks due to potential contamination in pre-training or fine-tuning datasets.
  arXiv  Detail & Related papers  (2023-11-08T17:35:20Z)
• Data Contamination Through the Lens of Time [21.933771085956426]
  Large language models (LLMs) are often supported by evaluating publicly available benchmarks. This practice raises concerns of data contamination, i.e., evaluating on examples that are explicitly or implicitly included in the training data. We conduct the first thorough longitudinal analysis of data contamination in LLMs by using the natural experiment of training cutoffs in GPT models.
  arXiv  Detail & Related papers  (2023-10-16T17:51:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.