Detecting Benchmark Contamination Through Watermarking
Track: long paper (up to 9 pages)
Keywords: LLM, Watermarking, Benchmark, Contamination
TL;DR: Watermarking benchmarks appear like an promising solution to the problem of contamination in LLMs: we can maintain benchmark utility while successfully identifying contamination, e.g. p-val =10^{-3} for +5% on ARC-Easy
Abstract: Benchmark contamination poses a significant challenge to the reliability of Large Language Models (LLMs) evaluations, as it is difficult to assert whether a model has been trained on a test set. We introduce a solution to this problem by watermarking benchmarks before their release.The embedding involves reformulating the original questions with a watermarked LLM, in a way that does not alter the benchmark quality and utility. During evaluation, we can detect ``radioactivity'', i.e. traces that the text watermarks leave in the model during training, using a theoretically grounded statistical test. We test our method by pre-training 1B models from scratch on 10B tokens with controlled benchmark contamination, and validate its effectiveness in detecting contamination on ARC-Easy, ARC-Challenge, and MMLU. Results show similar benchmark utility post-rephrasing and successful contamination detection when models are contaminated enough to enhance performance, e.g. p-val =$10^{-3}$ for +5% on ARC-Easy.
Presenter: ~Tom_Sander1
Format: Yes, the presenting author will definitely attend in person because they are attending ICLR for other complementary reasons.
Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.
Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.
Submission Number: 28
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