Language model watermarking influences which plausible tokens a model selects without inserting visible characters or metadata. A secret key and contextual information define preferred choices, and repeated preference across a passage creates a signal that can be distinguished from ordinary sampling.
The method trades off detectability, text quality, robustness, and access. Short or predictable outputs contain less room to encode a pattern, paraphrasing can weaken it, and key-based detection usually establishes compatibility with one watermarking system rather than proving authorship in every possible case.
Acronyms and aliases
LLM watermarking acronymAI text watermarking variant
Specialised terms
Related terms
Frequently asked questions
Is a language model watermark visible in the text?
Usually not. It is encoded through statistical token choices rather than a visible symbol, character sequence, or conventional metadata field.
Can language model watermarking prove who created a passage?
Not by itself. Detection can support provenance for a specific keyed system, but errors, editing, short text, and key access affect certainty.
