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This research introduces a novel watermarking technique using invisible Unicode characters to audit large language models for the unauthorized use of copyrighted or sensitive data during fine-tuning. By leveraging cue-reply pairs, the system achieves high detection rates with minimal invasiveness, offering a scalable solution for verifying data provenance in black-box scenarios. This advancement directly addresses critical challenges in AI safety and intellectual property protection within model training pipelines.
Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
arXiv:2510.09655v3 Announce Type: replace-cross Abstract: We propose a system for marking sensitive or copyrighted texts to detect their use in fine-tuning large language models under black-box access with statistical guarantees. Our method builds digital ``marks'' using invisible Unicode characters organized into (``cue'', ``reply'') pairs. During an audit, prompts containing only ``cue'' fragments are issued to trigger regurgitation of the corresponding ``reply'', indicating document usage. To control false positives, we compare against held-out counterfactual marks and apply a ranking test, yielding a verifiable bound on the false positive rate. Empirically, we obtain a true positive rate of 96.7% at 0% false positive rate and reply regurgitation rates exceeding 28% per document with only 40 (4%) watermarked documents. The approach is minimally invasive, scalable across many sources, robust to standard processing pipelines, and achieves high detection power even when marked data is a small fraction of the fine-tuning corpus.
Abstract:We propose a system for marking sensitive or copyrighted texts to detect their use in fine-tuning large language models under black-box access with statistical guarantees. Our method builds digital ``marks'' using invisible Unicode characters organized into (``cue'', ``reply'') pairs. During an audit, prompts containing only ``cue'' fragments are issued to trigger regurgitation of the corresponding ``reply'', indicating document usage. To control false positives, we compare against held-out counterfactual marks and apply a ranking test, yielding a verifiable bound on the false positive rate. Empirically, we obtain a true positive rate of 96.7% at 0% false positive rate and reply regurgitation rates exceeding 28% per document with only 40 (4%) watermarked documents. The approach is minimally invasive, scalable across many sources, robust to standard processing pipelines, and achieves high detection power even when marked data is a small fraction of the fine-tuning corpus.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2510.09655 [cs.CR]
(or arXiv:2510.09655v3 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2510.09655 Focus to learn more arXiv-issued DOI via DataCite
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