Thread

Understanding the Impact of LLM Watermarking on AI Agent Behavior

58 points · 72 comments · nisosguy

WithinReason · 1790430559 · focus · HN ↗
This is getting tiring. Watermarking has no effect on model output quality when implemented correctly. It's somewhat like swapping a random RNG seed to the seed 42, and detecting what the seed was from a random sequence. The sequence generated from the seed 42 is just as random as any other seed. There couldn't be a quality difference. And yes, the output from an LLM is a conditional random sequence of tokens from a distribution determined by a model.
1. nonethewiser · 1790434301 · focus · HN ↗
  > This is getting tiring. Watermarking has no effect on model output quality when implemented correctly.
  Over a certain token threshold, yes, there are 0 negative effects. Something like 300-400 words. At the boundary and below it, it does effect response quality, so they don’t (shouldnt) do it. It also incentivizes increasing tokens in low token responses so that it can be watermarked which is it’s own quality issue.

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