Sample Complexities of Estimating Gumbel--Max Watermark Proportions with and without Reduction to Pivotal Statistics
arXiv:2607.00224v1 Announce Type: cross Abstract: Watermarking promises a statistical trace of large language model (LLM) use, but real documents, after editing or paraphrasing, rarely arrive as purely human-written or purely machine-generated. This motivates a quantitative question beyond detection: what proportion of a document is generated from a pre-specified watermarked LLM? We study this watermark proportion estimation problem under the Gumbel--max watermarking mechanism, treating the next-token prediction (NTP) distributions as unknown and arbitrary nuisance parameters subject to a non-
The proliferation of advanced large language models (LLMs) and growing concerns about their provenance and potential misuse necessitate robust methods for identifying machine-generated content.
This research directly addresses the critical need for verifiable authenticity and intellectual property tracking within AI-generated content, impacting trust, accountability, and industry standards.
The ability to accurately estimate the proportion of watermarked LLM content within a document provides a nuanced measure beyond simple detection, enabling better content governance and attribution.
- · Content Authenticity Initiative
- · News organizations
- · Regulatory bodies
- · LLM developers implementing watermarking
- · Malicious misinformation actors
- · Plagiarists
- · Undetectable AI content generators
Improved methods for auditing and verifying the origin of digital text content.
Increased pressure for LLM developers to integrate effective watermarking technologies into their models from inception.
The development of a new forensic science discipline dedicated to analyzing and attributing AI-generated content in legal and ethical contexts.
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.LG