From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models
arXiv:2603.04828v2 Announce Type: replace Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly focus on the likelihood-based statistical features or heuristic signals before and after fine-tuning, but the former are susceptible to word frequency bias in corpora, and the latter strongly depend on the similarity of fine-tuning data. From an optimization perspective, we observe that during training, samples transition from unfamiliar to familiar in a manner reflected by systematic differences
The proliferation of increasingly opaque large language models necessitates new methods for understanding and controlling their origins, especially as concerns regarding copyright and data integrity escalate.
Detecting pre-training data is crucial for addressing intellectual property rights in data used for LLMs and for preventing bias or manipulation from undisclosed sources, directly impacting trust and regulation.
New methodologies for auditing LLM training data are emerging, moving beyond statistical features or heuristic signals, which could lead to more robust methods for model provenance and accountability.
- · Content creators and copyright holders
- · LLM auditing and provenance firms
- · Regulators and policymakers
- · Academic researchers in AI ethics
- · LLM developers with opaque data practices
- · Entities engaged in data scraping without consent
- · Users relying on unchallenged LLM outputs
Improved methods for pre-training data detection will enhance transparency and accountability in large language models.
Increased transparency could lead to new legal challenges and regulatory frameworks for data usage in AI training.
The ability to accurately attribute training data may foster new markets for licensed, high-quality datasets and more ethical AI development practices.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL