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Research questionHow can watermarking provide trustworthy provenance for LLM text at scale despite transformations and accumulating false positives?LLM outputs can be copied, transformed, and redistributed without dependable evidence of origin. Watermark signals may weaken through repeated transformations, while even small false-positive rates become consequential when detection operates at large scale.
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Latest papersRecent research connected to this question, newest first.LLM Watermarking as Big Data Provenance: A Deployment-Oriented SystematizationThe source systematizes LLM watermarking as provenance infrastructure, organizing approaches by insertion point, verification authority, operational state, and transformation threats. It addresses online generation, streaming detection, transformation pipelines, and ecosystem governance, and provides an evaluation blueprint rather than evidence for one specific watermarking implementation.research paper · Sep 2, 2026
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