Thread

1. 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. AlotOfReading · 1790435201 · focus · HN ↗

      Accepting your analogy at face value, it's still not obvious to me that fixing a specific seed doesn't change things.

      Take a recurrent PRNG for example. A randomly seeded recurrent function usually has degenerate cycles in its state space. For some functions, this might even describe the majority of the state space. This is why so many non-cryptographic PRNGs are max-cycle, so a different starting point is just further along the same trajectory.

      I don't think LLMs have quite the same failure mode here, but recurrence + high dimensional spaces triggers my "here be dragons" sense.

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