Watermarking Text Generation Efficiently

Author(s): Enzo Lombardi Originally published on Towards AI. Or Why What You Recently Read About AI Watermarking Is Probably Wrong Most explanations of AI watermarking describe something that does not exist. They talk about hidden Unicode characters smuggled between words, or invisible zero-width spaces, or secret vocabulary the model is forced to use, or a classifier that has learned what machine prose smells like. Some of those are real techniques for other problems. None of them is how watermarking actually works in the systems that ship it, and the confusion matters, because every one of those imagined mechanisms would be defeated by pasting the text into a plain editor. After the introduction, the article explains that real watermarking relies on biasing a model’s token sampling: instead of altering the written words directly, it nudges the “slack” in each next-token choice so the resulting text carries a statistical signature. It walks through the classic “green list” method (hashing the previous token plus a secret key to split the vocabulary each step, then adding a delta to logits for “green” tokens), shows how detection becomes a coin-flip-style z-score test that only ne…