That's inaccurate in two ways:
(1) The behavior that is approximately what you describe is not "fundamental" (though it may not be something you can disable on some hosted providers), it is an option that is not fundamental (and with runtimes where you have full control can be either disabled or tuned in a large number of manners), and
(2) The actual behavior that is approximately what you describe already usually involves use of PRNG (with a user or harness supplied seed), not a true RNG; the change to do watermarking isn't going from RNG to PRNG, it involves adding an additional set of constraints on token generation on top of the existing ones, which inherently compromises quality.
You're generating a pseudo random number one way instead of another way. How would that inherently compromise quality?
Same reason that you don't just replace your rand() implementation with "return 4; // chosen by fair dice roll". If you need randomness for whatever reason, biasing the generator is compromising quality.
( https://xkcd.com/221/ )
In case of LLMs, you can look at it from high and low level.
At low level - if you could do with less randomness, you can always lower temperature. You usually keep it (or for SOTA providers' chat UI, they keep it) at a level where it's about right level - high enough to allow for more creative leaps and interpretations, low enough that it doesn't go off into crazy land after the third paragraph.
At high level - creativity is driven by randomness. If you had an author (fiction or nonfiction) you like for their both broad and deep range of insightful thoughts, would you be happy if they suddenly developed an acute porn obsession and uncontrollably added lewd subtext to every other sentence? Still creative, still deep, but now with that one strong attractor that biases their every thought in a single direction? Would you trust/enjoy their output as much as you did before?
That, slightly exaggerating to make it more obvious, is what "loss of quality" means here.
1) we’re not discussing those systems. We’re discussing a chat AI product called Claude, which does not offer those knobs.
2) Claude’s PRNG having a P is immaterial
Claude has those knobs, they are just not exposed to the user. They could make Claude nearly completely deterministic if they wanted to (of course it would be a far inferior product then. But they could).
(1) LLMs collapse and start outputting garbage after a number of tokens if you do not sample and just pick the "best token" each time. This is a consequence of how they are trained.
> LLMs collapse and start outputting garbage after a number of tokens if you do not sample and just pick the "best token" each time.
LLMs are likely to get stuck even with sampling if asked to generate tokens on their own long enough, though sampling does tend to stretch out the time before that happens (as do other techniques that don't involve sampling, like applying repetition penalties directly to token logits). But LLMs generally aren't left to infinitely extend their own output, and the length response typically needed in the use case is much shorter than the would result in collapse given the kinds of inputs expected in that use case, the existence of the theoretical eventuality may not really matter.
Citation needed
You know you can just try it and see on any inference system thst has this knob, right?
Related: if you don't have a limit on sampling (top-K or top-P), eventually you'll hit one of the really unlikely tokens by chance and then the model will switch to Japanese because the most likely completion after a random Japanese character in the middle of an English sentence is more Japanese writing, not a reversal back to English.
That could be how it works, but in practice it takes into account all previous tokens when producing the next-token distribution to sample from. So a switch back is more likely than your explanation supposes.
> which inherently compromises quality.
I don’t see how this follows? Tokens are chosen randomly. If you choose tokens with a different RNG in the same distribution, you’re still getting equally good or bad tokens.
Not all values of "equally good" are equally good.
Writing has rhythm, or at least it's supposed to, and synonym swapping compromises it.
Never mind metaphors and similes, which are even more tightly constrained.
LLM writing is still a long way from good. Sometimes you get lucky with the odd line, but there's a difference in quality between influencer slop, genre fiction, and literary fiction and/or best-in-class journalism.
LLMs are still somewhere between the first two, and nowhere close to approaching the third.
> Not all values of "equally good" are equally good. Writing has rhythm, or at least it's supposed to, and synonym swapping compromises it.
We already know that a non-zero temperature improves quality though with current models (particularly with creative writing). The assumption that always picking the 'best' token results in the 'best' output is not the current reality.
And if you are already intentionally putting in randomness, I can imagine that it would be possible to seed the randomness in a way that is detectable but results in the same quality.
This is obviously not true for queries where temp = 0, but at temp = 0 then it becomes easier to identify anyway. I assume this technique implies some level of temperature.
It's not just swapping synonyms. The way llms work is by predicting the likelihood of the next token. It's inherently probabilistic. Choices are made based on weighted random number generation, based on those probabilities. Changing how you generate the random numbers doesn't degrade the output.
wow even on HN people have no clue how any of that works? all LLM generation has some inherent randomness to it, if you replace part of that randomness to be deterministically random the result of the generation with the fingerprint and without it, is INDISTINGUISHABLE. This has absolutely nothing to do with "synonym swapping". Its also again people not understanding how anything works missing the real concern, which is that nobody can ever tell if it isn't also secretly a fingerprint with the user and session id.