Text watermarking has no effect on output quality, it just works by changing the explicit source of randomness that is in practice always present in LLM output sampling. See for example https://www.seangoedecke.com/ai-text-watermarking-is-not-a-b....
> Text watermarking has no effect on output quality
It has an effect, and it's negative. It's hoped that the effect is negligible, and it probably is, but the whole point is that it has an effect.
Its essentially swapping out the psuedo random number generated with a differently seeded one iirc.
It has an effect on the output, but not the output quality
umm so like wait,
if I ask it to paint with a shade of red, but it paints with a slightly different shade of red, that is a fucking effect on quality
If you type like Joey using a thesaurus for the first time, it has an effect on quality
It reduces diversity, which they don't talk about much. Wrote about it here. https://blog.keyvan.net/p/ai-text-watermarking-and-quality
It seems fine to me. The model is still solving my problems and writing code that works as well as any other.
Google has been watermarking text with SynthID for a while now and nobody complained about it. Why all the fuss about Claude?
It feels like the real reason behind most complaints is that people want to use AI for writing and not have others find out?
It has an effect, 50% of the time it's positive, increasing the quality of the answer, and 50% of the time it's negative.
These models are already running with a temperature that is >0. This just replaces a random number with a slightly less random number.
That was my point, replacing one random sampling by another has exactly that effect.
I am pretty sure they did A/B testing to show it didn't. I could gave sworn they even released a quiz were the user has to try and guess which answer is watermarked or not and it was impossible to tell.
That's not the metric, though.
You either output the best version, or you output something else.
You can't do both.
That's not the case, because LLMs are non-deterministic.
It only alters outputs when the last layer of the neural network give significant weights to multiple tokens, and it would anyway have picked a random answer.
Instead it picks a non-random one, but non-random in such a way that you can't tell without the private key of the watermarking.
This mostly adds randomness these days for branches in syntax that make no difference, and the model has no reason to believe make a difference. Anything that matters, it is much more confident in the last layer of weights on the token to use.
I feel as though you are overlooking simple statistics/confidence intervals. It absolutely possible for two different works to be not have a distinguishable difference in quality.
Agreed, but not if one of them was altered to contain a secret message. That one will have a distinguishable difference in quality. Maybe (almost certainly) negligible, but still there.
This...isn't true.
Others have already said this, but the watermarking is something like "when the model flips a coin picking between two values, always choose heads". It was already flipping a coin. You're not choosing a less good result, you're just using a deterministic process when it was stochastic before.
This will have some impact on outputs, but unless you have some reason to believe that always picking tails was better than always picking heads (in which case, you should be working at one of these companies in model training!) it won't have any impact on output quality.
that's fair. but it outputted non-best version in the first place sometimes too. Now it still output non-best version sometimes.
Why do you claim that?
There is no reason why there has to be a negative effect of text watermarking.
It literally re-weights the output tokens from what the LLM would otherwise have chosen. It _has_ to. It can't be positive, because then that's not watermarking, it's a better LLM.
It's a very unintuitive algorithm, and is pretty clever.
I recommend reading up on it: https://www.nature.com/articles/s41586-024-08025-4
But no, it only ever picks tokens that are in the probability distribution of the last layer, and it might have picked anyway.
What if the next token represents a wrong or low-quality answer, but would have only been picked 10% of the time, but now it's picked 20% of the time? Doesn't that obviously decrease the model quality, even though "it might have picked that token anyway"?
Unless you’re at 0 temperature, there is no single token it would have chosen. It’s always picking one of multiple randomly according to a probability distribution.
Give me an example how would you watermark a single short sentence like "I like turtles"?
Watermarking just alters the pseudorandom number generator. If "I like turtles" was previously the response to your prompt with probability 100%, it will still be so. This is why watermarking is only effective for long strings of text
Unless you’re running at temperature 0, there’s not one single token that the model definitely would have chosen each time.
This is hilarious this keeps being repeated by the true believers ad nauseam.
Also, don't apply EU law to the world. It's a knee jerk reactionary regulation by a bunch of aging ding dongs that can't print their emails.
You're on Hacker News - I suggest you have technical curiosity and actually understand this very unusual and innovative algorithm, before you claim things about it that aren't true.