CAP: Data Contamination Detection via Consistency Amplification

• URL: http://arxiv.org/abs/2410.15005v1
• Date: Sat, 19 Oct 2024 06:33:33 GMT
• Title: CAP: Data Contamination Detection via Consistency Amplification
• Authors: Yi Zhao, Jing Li, Linyi Yang,
• Abstract summary: Large language models (LLMs) are widely used, but concerns about data contamination challenge their reliability. We propose a novel framework, Consistency Amplification-based Data Contamination Detection (CAP), which introduces the Performance Consistency Ratio (PCR) to measure dataset leakage. CAP is applicable to various benchmarks and works for both white-box and black-box models.
• Score: 20.135264289668463
• License:
• Abstract: Large language models (LLMs) are widely used, but concerns about data contamination challenge the reliability of LLM evaluations. Existing contamination detection methods are often task-specific or require extra prerequisites, limiting practicality. We propose a novel framework, Consistency Amplification-based Data Contamination Detection (CAP), which introduces the Performance Consistency Ratio (PCR) to measure dataset leakage by leveraging LM consistency. To the best of our knowledge, this is the first method to explicitly differentiate between fine-tuning and contamination, which is crucial for detecting contamination in domain-specific models. Additionally, CAP is applicable to various benchmarks and works for both white-box and black-box models. We validate CAP's effectiveness through experiments on seven LLMs and four domain-specific benchmarks. Our findings also show that composite benchmarks from various dataset sources are particularly prone to unintentional contamination. Codes will be publicly available soon.

Related papers

• Evaluation data contamination in LLMs: how do we measure it and (when) does it matter? [10.691754344782387]
  It is difficult to define precisely which samples should be considered contaminated, and how it impacts benchmark scores. We propose a novel analysis method called ConTAM, and show with a large scale survey of evaluation data contamination metrics. We find that contamination may have a much larger effect than reported in recent LLM releases and benefits models differently at different scales.
  arXiv  Detail & Related papers  (2024-11-06T13:54:08Z)
• Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges [3.0455427910850785]
  We evaluate five contamination detection approaches with four state-of-the-art LLMs across eight challenging datasets. Our analysis reveals that current methods have non-trivial limitations in their assumptions and practical applications.
  arXiv  Detail & Related papers  (2024-09-16T02:04:33Z)
• 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)
• Anomaly Detection of Tabular Data Using LLMs [54.470648484612866]
  We show that pre-trained large language models (LLMs) are zero-shot batch-level anomaly detectors. We propose an end-to-end fine-tuning strategy to bring out the potential of LLMs in detecting real anomalies.
  arXiv  Detail & Related papers  (2024-06-24T04:17:03Z)
• Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models [42.958880063727996]
  CDD stands for Contamination Detection via output Distribution for LLMs. To mitigate the impact of data contamination in evaluation, we also present TED: Trustworthy Evaluation via output Distribution.
  arXiv  Detail & Related papers  (2024-02-24T23:54:41Z)
• 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)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)

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.