arXiv:2601.19936v2 Announce Type: replace Abstract: The opacity of massive pretraining corpora in Large Language Models (LLMs) raises significant privacy and copyright concerns, making pretraining data detection a critical challenge. Existing state-of-the-art methods typically rely on token likelihoods, yet they often overlook the gap between the target token and the model's top-1 prediction, as well as local correlations between adjacent tokens. In this work, we propose Gap-K%, a novel pretraining data detection method grounded in the optimization dynamics of LLM pretraining. By analyzing the
The increasing scale and opacity of LLM pretraining data, coupled with rising concerns over privacy and copyright, necessitate more robust detection methods.
The ability to accurately detect pretraining data fundamentally impacts intellectual property, data governance, and the ethical development of AI.
New methods like Gap-K% offer more refined techniques for identifying pretraining data, potentially enhancing accountability and mitigating risks for LLM developers and users.
- · IP holders
- · Data privacy advocates
- · AI ethics researchers
- · Governing bodies
- · LLM developers using unvetted data
- · Data pirates
- · Users of models with undisclosed data provenance
Improved detection of copyrighted or private data in LLM training sets.
Increased pressure on LLM developers to use transparent and ethically sourced pretraining data.
Potentially, new regulatory frameworks requiring auditable data provenance for commercial AI models.
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Read at arXiv cs.LG