alt Hacker NewsOne aspect that people seemingly aren't talking about is the impact this has in the model's creativity. Because the model will nudge each word towards group A vs group B, you're losing on creativity, especially more so if the nudge isn't a gentle 55% but something like 70% or 80%. So essentially they're forcing the model to be less creative for the upside that the longer the text the easier it is to detect the watermark.
Here's a visual representation of the watermark: https://i.imgur.com/JNUIykX.png
This is even worse in such forms of writing like coding, where there's even less choices the model can make on what the next token should be. Plain text in code will obviously be watermarked, that includes comments. But the code itself might get watermarked by choosing certain code over others more often.
I'm inclined to believe the models will be instructed to not watermark code, especially since it's harder to detect reliably because the shorter the body of text the harder it is to detect, but who knows what Anthropic and all the other AI labs will decide to do in the future.
EDIT: Also for those in the comments who are naive enough to think Anthropic is doing this just because the EU said so and not because it's beneficial to them (and all other AI labs), well, you are indeed naive. Identifying code will be paramount in training future models because the more synthetic data you feed it, the more cannibalization happens, the worse the models will perform over time due to lack of good data, among other such reasons as selling AI detection services to colleges, and a plethora of other reasons.
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The details will depend on the exact implemention, but I don't think this necessarily effects output quality.
The models "natural" output is the result of a series of random numbers. The watermark works by biassing that series towards a different series of numbers. Assuming that second series is cryptographically secure psuedo-random, the even distinguishing the biased sequence from true random would be impossible with compromising the key or prng.
As an extreme, suppose your prompt was public, and the model seeded its PRNG with a secret key instead of a genuine random seed. Such an output is not meaningfully different from one based on a true RNG, but can be trivially fingerprinted by someone who knows the keys.
In practice, I am doubtful they have a scheme that is both practically useful and cryptographically secure. However, there is a lot of room below cryptographically secure that is still just as good for all other purposes.
It is worse than that, it can make text unintelligible. I have Claude explain what is happening in a PR, and the technobabble and use of rare words have to look up in a dictionary make it difficult to understand. I need another LLM to translate what the LLM is saying.
And I pray it is because of the EU AI Act, because it is worth giving them my personal information to prove I am not in the EU so they can turn this off. Heck, I will pay more to avoid this crap.
If it were not so noticable, I would shrug it off. But it has made things clearly worse this year.
As per the link, the words in the green and red groups are calculated dynamically, so it's not like the model is going to be told "use 'unique' over 'unusual'" and suddenly writing from the model will contain the word 'unique' far more often than 'unusual'. So I'm not sure it's clear that this has an impact on creativity as such?
That being said, I do question how this will apply to code as opposed to prose. Even data dense text (ie, if you ask Claude to evaluate what running shoe to buy, and it spits back a list of options with reviews and prices) may struggle.
What it probably will work well at it flagging the current tsunami of entirely AI generated novels on Amazon/Kindle, which is...honestly not without value.
> Identifying code will be paramount in training future models
True, but note that this strictly allows providers to identify text generated by their own models. If Anthropic wants to filter out GPT generated text in their training data, they'll need to feed it through an OpenAI API, which is implausible. So it might help on the margins, but I don't think it solves the problem of model collapse.