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If precise word choice and nuanced phrasing are the core priorities, handing off the writing process to an autoregressive model in the first place defeats the purpose. Using LLMs as a sounding board or for structural review avoids watermark exposure entirely, it only becomes detectable when someone is copying wholesale blocks of model-generated text.

The interesting part is that the detector doesn't need to identify a specific token choice. It can look for a small statistical skew across many choices. That also explains why the approach is fundamentally probabilistic: paraphrasing, translation, or human editing can dilute the signal without necessarily removing every trace of it.

The objection isn’t that normal sampling is somehow pure. It’s that watermarking uses token choice to carry an additional, covert signal. The quality cost may be small on average, but it is still an optimization constraint unrelated to meaning or style—and it turns ordinary prose into provenance metadata. The more serious questions are detection reliability, false positives, and what happens after human editing. https://prachub.com/

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> because the nature of the watermarking algorithm requires it to sometimes increase the probability of selecting a worse word choice and decrease the probability of selecting the model’s best choice.

... and the opposite is also true, sometimes it will increase the probability of choosing the "best" word choice. So watermarking makes the LLM quality better then? /s

> My initial speculation was that maybe they’d hide invisible non-printing Unicode characters in the text.

Or just em dashes? /s

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