Dataset Watermarking with Provable Black-Box Detection

Large Language Models (LLMs) are often pre-trained and post-trained on vast amounts of loosely curated data, and potentially have been trained on proprietary datasets or the benchmarks used for evaluation. This motivates dataset watermarking: designing datasets such that training on them leaves detectable signatures in the resulting model. Existing approaches embed watermarks by either injecting artificial content or paraphrasing full examples under token-level generation control, and many require model access beyond generated outputs for detection. Our method embeds a dataset-level watermark by increasing the co-occurrence of randomly selected word pairs through meaning-preserving local ...

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