> I want any LLM I use to choose the very best, most precise words at every single decision point.
Then bad news: LLMs already use randomness in a fundamental way. Each time they go to generate a token, they first generate a probability distribution of possible tokens. Then they pick one randomly according to this distribution. The technique described can be thought of as making the random number generator pseudo random. The output it generates is one of the possible outputs it would have generated before, just now it's deterministic and will generate the same thing every time.
I think this is a key reason why humans write better prose than LLMs - we can try to choose the best word every time, and go back and restructure sentences and paragraphs if we want.
On the other hand, LLMs are forced into picking some likely-ish word, and then have to build the rest of their response to retcon that choice into making sense.
Even good human writers would probably struggle with this constraint. It would be like someone interrupting your writing to tell you the next word MUST be such-and-such, and then you have to try and make it work as best you can first try, without going back to edit. The result would probably be a little clunky. (Maybe it’s impressive LLMs write as well as they do.)
This is the classic misunderstanding that LLMs only pick the next token at a time. Really, they are coalescing the probabilities of a range of tokens at a time. There is no “oops, I wrote ‘th’ but I should have written ‘tw’ so I guess I’m stuck writing three instead of tween”.
No, they really do one at a time. You're incorrect on that.
Mathematically, a long chain of conditional probabilities is equivalent to a single probability over the whole range. But computationally, for that to work out, the computation for the first probability needs to somehow consider all the downstream probabilities depending on it, which obviously isn't how autoregressive language models work. They can pack in as much downstream computation as their neural architecture allows for, which is quite a lot.
Suppose in some context you have three equally plausible conpletions after "Be": "tween a rock and a hard place", "twixed he stood there" and "lieve he can fly". To model this probability distribution of the whole sentence, the next token "tw" needs to appear at 2/3 probability and "lie" at 1/3. After "tw" would be a 1/2 chance of "ix" and a 1/2 chance of "een"; after "lie" would be a 100% chance of "ve " and in any case the rest of the sentence after that would be 100%.
The model needs to somehow "think ahead" to know those are the possible completions. For example if "lieve he can swim like a dolphin" was another equally plausible completion, that first token would need to be 50/50 instead of 67/33. So the computation of the first token somehow needs to encode the fact that the guy thinks he can fly but not swim, even though it doesn't become relevant in the output until several tokens later.
In practice this probably happens to some degree but definitely doesn't happen perfectly. To perfectly model the first token's probability distribution, it would have to include knowledge of the entire distribution of all possible outputs, which is just not happening. So it approximates. Surprisingly, the approximation is good enough to produce language.
You can see this breaking down in the seahorse emoji incident from last year. When you ask the model if there's a seahorse emoji, it first completes "Yes," as if a few tokens later it's about to produce a seahorse emoji. But when it actually gets to the token that would produce a seahorse emoji, it can't because there isn't one. But it's already outputted "Yes, the seahorse emoji is" and can't just go back and change that to "No, there's no seahorse emoji." Some models would try a few times and then say there isn't one or a system error seems to be making them unable to produce one, other models (including then-current ChatGPT) would loop forever with ensuing hilarity.
But on some level there is uncertainty, right? Even if it’s not token-specific but T the word- or phrase-level? Otherwise what does the temperature setting do? Or has architecture changed significantly in the background?
It's a bit like trying to finish a sentence when you're really stoned... you vaguely remember the preceding couple of words you've said but don't really know how you got there and now you're wandering in the forest trying to stumble on coherency.
Well, I suppose it's nearly the opposite of that experience, upon further review. But for some reason, that's where my head jumped.
This was true in the ChatGPT era. Now we're in a world with reasoning tokens, where a model can thoroughly plan out the response it wants to make. If anything, it makes the style worse.
Yes, models can reason and plan, which helps them write more coherently. But when they write the final output, it’s still a single generation. It would be like letting a human make notes and write an outline, but not let them use the backspace once they start typing their response.
Presumably you could use the same reasoning trace, run multiple generations, and get different outputs (if the temperature is >0).
But now I’m interested in playing more with Cowork or Claude Code/Codex for prose writing to see if the set of tools there affects outputs at all. I guess you might need a more custom “writing” harness.
autoregressive generation doesn’t mean the model is myopic. the next-token distribution can already reflect a longer horizon plan for the output sequence.
Sure, but mightn’t there be several plausible long horizon plans?
Here’s an example: I had asked Claude for some music recommendations in a certain style. Part of its output was:
—
*Long journey tracks*
Clinic — “The Return of Evil Bill”
Guided by Voices — not really, wrong band
Silver Apples — “Oscillations”. Proto-everything, deeply repetitive, hypnotic.
—
So at some point there, the next token produced was “Guided” or “Guide” or whatever, and then because it can’t go back, it had to correct itself after the fact.
Reasoning/CoT have helped a lot, but I feel like small versions of this still happen all the time.
Would be fun to run an LLM on fake output from itself. Like just force the first N tokens to say the beginning of something really stupid, and then see how it finishes the sentence. "You're absolutely right! Human feces is actually the most effective engine coolant because $<completion>"
Today I learned a new word, "Oulipo". Interesting.
But what about the general idea that they can watermark results to tell where they came from. The next step is tracking down which user got a result. I hate both of these things. Must everything we do be tracked? Next altering wikipedia results so they can tell who looked at the page or something?
I'd like "the best answer" from an llm and don't want to be tracked, but this isn't for me, it is for them. I understand llm results are already using a varying statistical input so they aren't always the same. But I really hate watermarking and likely tracking too.
(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.
(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.
Yeah this is my main issue with the argument.
He acknowledges in the article that LLMs are already non-deterministic, but he doesn’t seem to actually understand that.
I think the article is wrong on this but it's more subtle than that. Probability distributions have a peak; there is still a token with a peak probability. What's interesting about these techniques is that token by token it can actually make the peak token even more probable. A distribution doesn't have to be "flattened" to leave a watermark - it can be "amplified" and made "more peaky".
That's missing the point. It's the distribution that's the "best", not the tokens. Then Anthropic comes in and makes the distribution something other than the best. The only saving grace is that Anthropic says it's not that bad.
Even so, I don't think it will stop here. Once this is in place, the next step is to put more and more identification into the AI generated content; might as well pack it in, it's not that bad, and if it is they won't admit it. There's no way for anyone to check. And your argument will still be technically correct but missing the point.
How do you know you picked the singular “best” set of tokens in your comment here?
Could it have been equal or better with slight variations in wording?
The slipper slop argument is too lazy to address directly. Argue A is bad because A, not because A might become B and you’ve got good arguments against B.
Crazy how a smart person like this fails to understand the gumbel softmax technique. It does not affect writing quality at all, provably. The very fact that there is generally no "best next token" with 100% certainty is precisely why the trick works (you cannot watermark a response to "respond with the To be or not to be soliloquy from the first folio Hamlet", for precisely this reason).
It seems to me like he started out mad and looked to justify it.
I'm skeptical that anybody generating LLM text is really all that concerned about optimal word choice. Or even particularly good prose. But let's pretend that person exists.
If that person tried, say, an open model and that same model with watermarking applied, I'd be eager to hear their thoughts on the prose quality. Especially if they built an experiment harness and rated a few hundred blinded examples and found a measurable difference.
But getting this upset in advance of any demonstrated problem? It really seems to me like the point isn't the point
If this is true then the probability of the detection tools flagging completely human generated text as AI generated is non-trivial. Let's say I write a completely original piece and the detection tool says there is a 36% probability it was generated with Claude. What then? Now it's up to the person looking at the score to cast a subjective judgement. Maybe to me, anything over 25% is unacceptable. Maybe to someone else, it must cross over the 50% threshold. This is the problem.
> the probability of the detection tools flagging completely human generated text as AI generated is non-trivial
How does that follow? AI-generated text is already not a perfect emulation of human writing. There's lots of room to affect it laterally without changing the level of quality.
As I understand it, LLMs with temperature >0 can select from many possible outputs. All they're doing is limiting the possible outputs to ones that contain this pattern. I don't see any reason why the quality of that subset should be lower than average. The very best outputs will likely be eliminated, but so will the very worst.
I don't think that's true. I think it's a binary 0% or near 100% probability of a watermark having been detected; the more changes to the text having been made after the text was output by the LLM and the less leeway the LLM had for probable word choices, the longer the passage necessary to see it.
The "problem" is that seeing the watermark doesn't mean that the person claiming to be the author didn't make extensive changes to the output of the LLM, or that the LLM wasn't simply the final editor of something that the author had put a lot of work into.
> Cognitive surrender.
I don't know what this means. It's just drama. Don't let the LLM write for you and this is not a worry. I'm not worried about the poetry of LLM output being subtly adulterated.
I think he’s still generally good on business, UX, and hardware design. That’s all subjective and taste I suppose, but his taste works for me.
On deeper tech stuff, like this utterly nonsensical misunderstanding of watermarks… yeah, classic case of a guy who is smart, and has lost the ability to realize when they’re not knowledgeable in a domain.
> Does the author think he is currently getting T=0 output from Claude? Is he under the impression that T=0 produces the "best" writing?
No and no. I am not sure I agree with his point but I know he is not ill-informed on either of these points, because I mentioned them to him a couple of days ago.
It’s fully possible I didn’t explain it very well in the first place, but he is making a wider point.
The point I made (quite briefly) is that watermarking is only feasible because for good writing it is necessary to use T>0, or the writing will never explore a more creative choice, and that at T=0 you don’t even need a watermark to spot LLM-generated text.
The point he is making is consistent with this, isn’t it? Either you allow temperature to drive creativity, consistently in a way that can be influenced and analysed, or you adulterate that process for the purposes of meeting a corporate/legal directive, in a way that is proprietary and obscure. These are ethically distinct approaches, and since he disagrees with the EU objective he comes down on one side I guess.
Me, I don’t care about the hypothetical enough.
Not least because I think Claude writes depressingly badly and I doubt any steganographic change will enrage me less.
This is not it, no. He is not using AI and it is not I think remotely in his nature to surrender that control. He is engaging with this on principle. Again I am not sure I agree with him, but then it’s a hypothetical because I am not going to get an LLM to write for me either.
Translation: No one can ever again use Claude for proofreading their own prose unless they’re willing to risk that the whole thing might be flagged as having been generated by Claude.
You know, back in the era when proofreaders were human, I never one met a proofreader who rewrote my text afresh, rather than annotating the text with a pen.
It's still possible to use Claude to proofread - highlight grammatical, flow, structure, logic errors and make simple suggestions for you to pick and choose or adapt as you wish. No watermarking will flag your text. No flaw accusations of LLM authorship will haunt you. All will be fine.
But if you want an LLM to rewrite your text, that's (a) not proofreading, and (b) should be flagged as LLM generated ... because it is.
The question is, when is the "pro writer" version coming that lets you control this behaviour but costs more? Like night follows day, this will happen.
They will need to dodge around the EU requirements but it will probably just come down to an alternative method to watermark or a contractual assurance you won't mis-represent the source of the text.
I'm not sure the quoted statement is true. Proofreading like "point to problems in the text", if you fix the problems yourself and don't copy-paste the solutions given to you, should still be safe, shouldn't it? So, human-written text should not be falsely flagged if you use LLM for proofreading.
And if you copy-paste the answers from LLM, I think it's only fair the end result gets flagged. You're not writing it yourself.
> And if you copy-paste the answers from LLF, I think it's only fair the end result gets flagged. You're not writing it yourself.
I wonder if it would even get flagged in that case, because wouldn't the probability distribution of a token when the LLM is suggesting an edit to your writing be different than the distribution of that token once it is in the context of the text it's editing?
It is quite just, if you think about it. Human works are copyrighted and protected at the moment of creation. All rights reserved. Yet, LLM outputs are uncopyrightable. Therefore, if Claude or any AI has processed my copyrighted work, the end result is uncopyrightable and in the Public Domain. The public has a right to know: is this a human copyrighted work, an LLM PD work, or is the human falsely claiming authorship in order to retain copyright?
A point of confusion for me, however: is every watermark unique? Is every algorithm for watermarking going to vary amongst models and amongst model versions? Will each model publisher keep this watermarking as a trade secret, that they alone can detect? If so, this can't scale! How do you detect "JoeBob 4.3 LLM" output? By querying every single model's watermark-detector? And if they all work by re-running the model and using tokens anew? That is extraordinarily wasteful.
If a watermark is not self-evident, or universally detectable, then it is no good. Take, for example, US currency. The security measures are published and well known. Any count-out room in retail has a big poster indicating how you can detect authentic US bills. Nobody has to accept non-US currency in the US, and so the only authenticity you need to worry about is your US bills alone. LLM watermarking has none of this in common. Currently sounding like a shitshow, if you ask me.
As I understand it, the current watermarking methods rely on a secret key, making the detection schemes a black box to anyone not in possession of the key. This means organizations like Anthropic are free to make any claim about authorship they want, true or not, and no one can call them on it.
That's an intriguing twist, isn't it? It could lead to a tug-of-war.
Working backwards: if it is possible to confirm 100% confidence that a chunk of text is LLM output, then it is "PD until proven otherwise". How can a human reliably assert human authorship of their source text? When all watermark tests fail? Is that proof of humanity now?
If a human proves human authorship, and LLM watermarking tests positive, then is that going to be considered a "derivative work" or not? What if there is an applicable license for the source work, such as "CC-BY-ND" that prohibits derivative works?
This has not been court-tested, and I expect that it will need testing at that level before we can have any assurances.
The watermark, if I'm understanding correctly, only proves that something has been touched at some point by a particular model, not that it was entirely generated by a particular model.
As for it being "uncopyrightable" if it were the output of an LLM, I think computer people are making a very aspie interpretation of a single decision. I think it's more that the LLM (and thereby its owners) cannot itself hold a copyright on its output, that output has to be touched by a person before it is copyrightable. A particular view from the top of a mountain can't be copyrighted, for example, but a photograph of that view can be.
I'm not sure it at all precludes a "robosigning"* sort of situation, where machines generate output, hired temps sign and claim that output, and immediately sign it over to the people who hired them (as a work-for-hire.) Copyright is stupid, artificial law, not logical.
> The watermark, if I'm understanding correctly, only proves that something has been touched at some point by a particular model, not that it was entirely generated by a particular model.
For the watermark to be detectable, the text needs to be like 75% AI generated.
If you have an LLM “touch” one section of the article, it’s not gonna be detectable.
This article feels slightly incoherent. You want high quality precise writing and to use an LLM to generate it? Feels like those are diametrically opposed
Exactly. The watermark is proportional to how much text is AI generated. Either the AI really just “fixed some typos” (not enough AI content to hide a watermark) or the AI did most of the writing (enough AI content to hide a watermark).
LLMs are already perversions of writing, so what else is new. Oh no, the over-long circumlocution generated by three autocorrects in a trenchcoat might be slightly longer because of this and maybe people will start noticing the subtle rhythms of vaguely peculiar word choices as yet another cue that you are wasting their time with machine-generated wordslop, what a terrible fate. Your long rambling walls of machine-waffling might be 37.05% longer than they need to be instead of the mere 36.58% longer they are now.
I moved to Sol for my writing and it is so so much better. But it makes more mistakes. I think they have different ideas of product but it seems OpenAI is going to follow Anthropic’s lead over the next year. I think I am going to put more effort into my writing skills to remove myself from this awful situation
> One of my fundamental problem with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter.
Then why are you using an LLM to write? They're not capable of understanding such nuance. They do pick randomly between two synonymous phrases, they do not use some super smart algorithm to pick the one that sounds the best.
This excuse doesn't hold any water at all - Occam's razor says the author is just super annoyed that his AI writing will be identifiable as AI writing.
> The provider must mandate in their terms-of-service that users not remove the watermarking.
So, you don't own the generated text, and can't use it freely then. What if I copy paste a section, or rewrite a section of text to my liking? What if I rewrite some lines of code that contains the mark?
Security theater, and vague enough to be used as a weapon against who the government wishes.
It seems fine. I use an LLM to argue with me prior to posting blog posts so that I don't post obvious incorrectness, but the UX element to it is that it constructs notes about various sections of the text and we talk about those. There's no way for the generated text to enter the blog unless I copy-paste it and I'm not going to do that because the entire point is for me to write it.
At the point that you're generating entire volumes of text from Claude you're not really trying to be a sophisticated writer. I don't see how it's going to hurt for it to choose random related words.
Some here are arguing that mechanisms used by LLM providers already derail the goal of "the very best, most precise words at every single decision point", therefore the author is misguided.
The author has expressed a preference. Assume that there is a sequence of tokens, such that it is considered the absolute best by the author. This particular method of watermarking makes it less likely to generate that sequence, by definition.
I feel their argument would have been clearer and stronger if they had spent more time exploring the alternatives, and whether these alternatives would be just as effective. It is trivially easy to remove invisible tokens.
Like it or not, there is a public good to being able to identify AI generated content, and a small degredation in quality is tolerable in my opinion.
I don't think anybody has to worry about this issue though. Manual writing, coding, and proof reading continues to be an option. Where AI output is nothing to be ashamed of, the tools are available. For everyone else, there will be LLM providers that ignore EU law.
If the author has preferences on their "own writing" that conflict with Anthropic's, then they should actually write it themselves rather than paying Anthropic to do it. Private companies don't owe you anything, even less so when they're beholden to laws in foreign jurisdictions.
>Assume that there is a sequence of tokens, such that it is considered the absolute best by the author
You can't assume that because if that was the case he'd already know what sentence to write, because that's what that means.
The notion of a best sentence requires a final cause, an end to write to. By their very nature that's not how LLMs work, so you can't 'degrade' them on that front. They can't lose a property they didn't have.
Claude's writing was already easy to recognize. The fact that Anthropic complied without complaint makes me wonder if they already watermark their outputs and used the opportunity to create goodwill. Presumably they want to avoid training their new model on text generated by the previous model, so they have reasons to be able to recognize AI-generated text.
I dont see how there would be remotely enough entropy in most model outputs for this to be close to feasible with any kind of accuracy.
Either they false positve on pretty much everything ever written, or the chances of catching a true positive is so low as to be useless.
Basically Cinavia for text, and that often falls over and is easy to remove even when there is megabytes of data streaming over a long period of time rather than 2 or 3 bits per wall of text, let alone what most people use claude for, when there is a strict dictionary and other tight output constraints.
> I want any LLM I use to choose the very best, most precise words at every single decision point.
Try running an llm like qwen 3.8 27B in Q8 locally with an intentionally very low temperature setting, it will write like a caveman crossed with a robot. You may find that an extremely literal output does not look pleasant to read for humans.
Excessively precise word choice does not result in something that looks like content written by, or palatable to humans. It looks like you gave a high school 12 grade student a science paper and told them to apply a thesaurus to at least one word in every sentence and replace it with something else.
I was initially surprised that Gruber was so invested in the "quality" of AI-generated text, which in my mind is an oxymoron. But really, Gruber's interest here is with the EU. This forms part of his ongoing attacks on the EU, all because they have been forcing Apple to align with regulations.
Watermarks are garbage because they may embed account id, IP address and deanonimize you. That's why we should be using open-weights LLM whenever possible.
Total variation distance has been measured to decrease as you scale a model, and that is the primary mechanism "watermarking" as discussed in the Anthropic announcement relies on. It becomes more difficult to reliably detect text as a fixed sample count without tweaking the distribution further. Either way, it's a minor problem that will be addressed over time, compared to the issue of who can detect this without guessing or developing their own sets: providers not releasing a way to detect any such watermarks without going through them makes this entire approach hostile to the public. The EU regulation on this subject is interesting, although again most certainly not the primary driver for these practices:
"1.1.2: Signatories will ensure that AI-generated or manipulated content is marked with an imperceptible watermark, with the exception of very short text. For free-form text longer than 200 tokens, watermarking still needs to be applied, even though it may have lower reliability compared to that of watermarking very long text"
A proper, effective and useful law would have required providers to regularly release datasets to run your own verification on any text released within a fixed interval of time, presumably once out of rotation. Instead, it only talks about exposing an user interface going through their own services:
"Signatories will ensure access to their detection solution through a user interface appropriate for the audience of end-users that may eventually be exposed to the content generated or manipulated by their AI system. [...] Any restriction to the access will be limited in time until more reliable and robust detection mechanisms have emerged and have been adopted as the state of the art for detection mechanisms for the watermarking of free-form text evolves."
Most interestingly, in line with the EU's mass-surveillance program, an alternative solution to watermarking where it may not be sufficient is also suggested, although only optional for now:
"Where appropriate and taking into account potential trade-offs related to privacy and security, as well as scalability challenges and costs, Signatories may implement as an optional supplementary measure fingerprinting or logging solutions for AI-generated or manipulated content which allow for checking whether content has been generated or manipulated by their AI system. For example, direct logging may be appropriate for text content, whereas fingerprinting approaches may be preferable for audio and visual content."
And what I mean by that is that companies that are at the top tend to make anti customer decisions because they have lost the concept that pleasing customers matters as priority one.
I think it's pretty dishonest of Anthropic to frame their watermark as EU regulation compliance. The EU regulation, from my understanding, requires AI content to be labeled for human viewers. In the meanwhile the Anthropic new release on the watermark says this.
> The difference between watermarked and un-watermarked text will not be distinguishable to readers
Which is to say, it does not actually meet the EU AI act requirements which require transparency to humans. Not to mention that if the detection requires access to the base models, it makes anthropic the only entity who gets the say on if a piece of text comes out of Claude. Anthropic is both the player and the referee here.
If there is one takeaway you should have from this fiasco it is that you should be wary of using tools that doesn't serve your needs and your needs only.
I don’t understand how this works for anything but prose. Is that the point? In any code or structured output, there just isn’t the flexibility, and depending on how the user requests the output be constrained there is even less (“answer only True or False”). So is it just chat responses? If I ask the API to tell me a story about Alice and Bob then it watermarks it, but when I ask it some implausibly constrained thing like write a story about Alice and Bob with each word starting in rotation with the letters alicebob, does it try to do so and hope there are roughly équiprobable tokens regularly?
I should have read that, it’s actually quite reasonable and I don’t really understand the objections in TFA having read it.
> One of my fundamental problem with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter.
Doesn’t make sense at all in light of the actual approach, they’re just choosing a different RNG. It’s not like they’re corrupting it by flipping words.
Should add I don’t support the watermarking and requiring it is idiotic.
I don't understand Gruber's points either, I wonder if there is some fundamental technical misunderstanding. Does he think that the logits should be sampled from in a "pure" manner without introducing any other bias? Does he know that there's already a sampling temperature, and that most providers have probably moved on to sampling strategies other than top-k? Does he know that the word choices have already been altered irreversibly during RLHF which is how you get the obvious Claudism like "load bearing" and "seams"?
Perhaps it would be useful to publish examples of samples with/without watermark. I'd suspect that the variability from simply sampling repeated times would dwarf any semantic differences you'd detect with the watermark.
gruber is really out of his element with ai commentary, I fully support the general skepticism but he's seemingly arguing against something he doesn't quite grasp
I'll be honest, who fucking cares? Why would you use AI to write for you and then complain that people know AI wrote the code?? If you know people wouldn't like it, why even try!?
Anthropic should just pull out of the EU at this point. Europeans who really want to use it can VPN, and if they did they wouldn’t be able to hide behind their various comical tech laws.
Nanny state nonsense indeed.
As a non American/non European resident all I see from the Europeans are 0 contributions to software progress at any large scale while they surely do a lot of crying and huffing and puffing and demanding. Lots of complaining and rule making but not a lot of creating is a bad look.
> the only acceptable answer for why an LLM should choose bananas instead of pineapple (or coconut, or guava, or papaya...) is that it has determined that it’s the best fit for the intended meaning, tone, and sentiment of the text.
It already fails. It randomly picks between close candidates. To help fool people into believing in intelligence claim, I guess.
"Anthropic's . . . Claude is a Perversion of Writing."
FITFY.
I have no sympathy for writers whining about what the AI is doing to 'their' writing. It's only your writing when you write it. There's any easy way to avoid this: don't fucking use it. Use you own brain.
I keep seeing an irritating misconception in this space, which is that the alternatives chosen by these algorithms are supposed to mean the same things as what they're displacing. That's not true, and not how LLM generation works. Complaints that two different choices don't mean the same thing miss the entire point.
Gruber has a ridiculous knee-jerk response to anything the EU does, so hardly a surprise he didn't come to the table with a sober facts-based response.
Not telling someone you used AI is a perversion of writing. Also agree that an AI proofreader should not claim authorship, but in most other cases, the AI is not reading your mind, it's only watermarking its own usage, and we kind of need more of that.
> I want any LLM I use to choose the very best, most precise words at every single decision point.
Then bad news: LLMs already use randomness in a fundamental way. Each time they go to generate a token, they first generate a probability distribution of possible tokens. Then they pick one randomly according to this distribution. The technique described can be thought of as making the random number generator pseudo random. The output it generates is one of the possible outputs it would have generated before, just now it's deterministic and will generate the same thing every time.
I think this is a key reason why humans write better prose than LLMs - we can try to choose the best word every time, and go back and restructure sentences and paragraphs if we want.
On the other hand, LLMs are forced into picking some likely-ish word, and then have to build the rest of their response to retcon that choice into making sense.
Even good human writers would probably struggle with this constraint. It would be like someone interrupting your writing to tell you the next word MUST be such-and-such, and then you have to try and make it work as best you can first try, without going back to edit. The result would probably be a little clunky. (Maybe it’s impressive LLMs write as well as they do.)
This is the classic misunderstanding that LLMs only pick the next token at a time. Really, they are coalescing the probabilities of a range of tokens at a time. There is no “oops, I wrote ‘th’ but I should have written ‘tw’ so I guess I’m stuck writing three instead of tween”.
No, they really do one at a time. You're incorrect on that.
Mathematically, a long chain of conditional probabilities is equivalent to a single probability over the whole range. But computationally, for that to work out, the computation for the first probability needs to somehow consider all the downstream probabilities depending on it, which obviously isn't how autoregressive language models work. They can pack in as much downstream computation as their neural architecture allows for, which is quite a lot.
Suppose in some context you have three equally plausible conpletions after "Be": "tween a rock and a hard place", "twixed he stood there" and "lieve he can fly". To model this probability distribution of the whole sentence, the next token "tw" needs to appear at 2/3 probability and "lie" at 1/3. After "tw" would be a 1/2 chance of "ix" and a 1/2 chance of "een"; after "lie" would be a 100% chance of "ve " and in any case the rest of the sentence after that would be 100%.
The model needs to somehow "think ahead" to know those are the possible completions. For example if "lieve he can swim like a dolphin" was another equally plausible completion, that first token would need to be 50/50 instead of 67/33. So the computation of the first token somehow needs to encode the fact that the guy thinks he can fly but not swim, even though it doesn't become relevant in the output until several tokens later.
In practice this probably happens to some degree but definitely doesn't happen perfectly. To perfectly model the first token's probability distribution, it would have to include knowledge of the entire distribution of all possible outputs, which is just not happening. So it approximates. Surprisingly, the approximation is good enough to produce language.
You can see this breaking down in the seahorse emoji incident from last year. When you ask the model if there's a seahorse emoji, it first completes "Yes," as if a few tokens later it's about to produce a seahorse emoji. But when it actually gets to the token that would produce a seahorse emoji, it can't because there isn't one. But it's already outputted "Yes, the seahorse emoji is" and can't just go back and change that to "No, there's no seahorse emoji." Some models would try a few times and then say there isn't one or a system error seems to be making them unable to produce one, other models (including then-current ChatGPT) would loop forever with ensuing hilarity.
But on some level there is uncertainty, right? Even if it’s not token-specific but T the word- or phrase-level? Otherwise what does the temperature setting do? Or has architecture changed significantly in the background?
There are diffusion-based models and transformer-based models (and many other "architectures"), so your comment does not make sense.
Are there any diffusion-based or otherwise non-transformer-based models in mainstream use?
If by mainstream you mean "ChatGPT" or "Claude", then no.
But also, no one really knows as they're closed.
everyone in this thread is saying something kind of imprecise and reductive and varies between models and even modifications among the models
It's a bit like trying to finish a sentence when you're really stoned... you vaguely remember the preceding couple of words you've said but don't really know how you got there and now you're wandering in the forest trying to stumble on coherency.
Well, I suppose it's nearly the opposite of that experience, upon further review. But for some reason, that's where my head jumped.
You're thinking of Markov chains.
This was true in the ChatGPT era. Now we're in a world with reasoning tokens, where a model can thoroughly plan out the response it wants to make. If anything, it makes the style worse.
Yes, models can reason and plan, which helps them write more coherently. But when they write the final output, it’s still a single generation. It would be like letting a human make notes and write an outline, but not let them use the backspace once they start typing their response.
Presumably you could use the same reasoning trace, run multiple generations, and get different outputs (if the temperature is >0).
But now I’m interested in playing more with Cowork or Claude Code/Codex for prose writing to see if the set of tools there affects outputs at all. I guess you might need a more custom “writing” harness.
Isn't this just chain-of-thought though, doing the same thing multiple times without necessarily defining one path?
Chain of thought attempts to guide a single forward pass.
Reasoning tokens with tool calling tell the model to loop on a one phase of a question and call a tool to indications completion when done.
Related, but not the same thing.
autoregressive generation doesn’t mean the model is myopic. the next-token distribution can already reflect a longer horizon plan for the output sequence.
It can but it is limited because it's only got a single pass through the network to fit the entire "longer horizon plan".
Sure, but mightn’t there be several plausible long horizon plans?
Here’s an example: I had asked Claude for some music recommendations in a certain style. Part of its output was:
—
*Long journey tracks*
Clinic — “The Return of Evil Bill”
Guided by Voices — not really, wrong band
Silver Apples — “Oscillations”. Proto-everything, deeply repetitive, hypnotic.
—
So at some point there, the next token produced was “Guided” or “Guide” or whatever, and then because it can’t go back, it had to correct itself after the fact.
Reasoning/CoT have helped a lot, but I feel like small versions of this still happen all the time.
Human writing is like 90% editing.
Would be fun to run an LLM on fake output from itself. Like just force the first N tokens to say the beginning of something really stupid, and then see how it finishes the sentence. "You're absolutely right! Human feces is actually the most effective engine coolant because $<completion>"
auto-oulipo
Today I learned a new word, "Oulipo". Interesting.
But what about the general idea that they can watermark results to tell where they came from. The next step is tracking down which user got a result. I hate both of these things. Must everything we do be tracked? Next altering wikipedia results so they can tell who looked at the page or something?
I'd like "the best answer" from an llm and don't want to be tracked, but this isn't for me, it is for them. I understand llm results are already using a varying statistical input so they aren't always the same. But I really hate watermarking and likely tracking too.
Models can easily do multiple passes
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.
(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.
Citation needed
You know you can just try it and see on any inference system thst has this knob, right?
Yeah this is my main issue with the argument. He acknowledges in the article that LLMs are already non-deterministic, but he doesn’t seem to actually understand that.
I think the article is wrong on this but it's more subtle than that. Probability distributions have a peak; there is still a token with a peak probability. What's interesting about these techniques is that token by token it can actually make the peak token even more probable. A distribution doesn't have to be "flattened" to leave a watermark - it can be "amplified" and made "more peaky".
That's missing the point. It's the distribution that's the "best", not the tokens. Then Anthropic comes in and makes the distribution something other than the best. The only saving grace is that Anthropic says it's not that bad.
Even so, I don't think it will stop here. Once this is in place, the next step is to put more and more identification into the AI generated content; might as well pack it in, it's not that bad, and if it is they won't admit it. There's no way for anyone to check. And your argument will still be technically correct but missing the point.
How do you know you picked the singular “best” set of tokens in your comment here?
Could it have been equal or better with slight variations in wording?
The slipper slop argument is too lazy to address directly. Argue A is bad because A, not because A might become B and you’ve got good arguments against B.
The watermark doesn’t change the distribution, only per-token selection. I think not understanding that is the source of most people’s FUD.
This comment disagrees with you: https://news.ycombinator.com/item?id=49324387
Crazy how a smart person like this fails to understand the gumbel softmax technique. It does not affect writing quality at all, provably. The very fact that there is generally no "best next token" with 100% certainty is precisely why the trick works (you cannot watermark a response to "respond with the To be or not to be soliloquy from the first folio Hamlet", for precisely this reason).
It seems to me like he started out mad and looked to justify it.
I'm skeptical that anybody generating LLM text is really all that concerned about optimal word choice. Or even particularly good prose. But let's pretend that person exists.
If that person tried, say, an open model and that same model with watermarking applied, I'd be eager to hear their thoughts on the prose quality. Especially if they built an experiment harness and rated a few hundred blinded examples and found a measurable difference.
But getting this upset in advance of any demonstrated problem? It really seems to me like the point isn't the point
Google has A/B tested watermarking on millions of responses. They say they observed no difference in user behavior.
If this is true then the probability of the detection tools flagging completely human generated text as AI generated is non-trivial. Let's say I write a completely original piece and the detection tool says there is a 36% probability it was generated with Claude. What then? Now it's up to the person looking at the score to cast a subjective judgement. Maybe to me, anything over 25% is unacceptable. Maybe to someone else, it must cross over the 50% threshold. This is the problem.
Cognitive surrender.
> the probability of the detection tools flagging completely human generated text as AI generated is non-trivial
How does that follow? AI-generated text is already not a perfect emulation of human writing. There's lots of room to affect it laterally without changing the level of quality.
As I understand it, LLMs with temperature >0 can select from many possible outputs. All they're doing is limiting the possible outputs to ones that contain this pattern. I don't see any reason why the quality of that subset should be lower than average. The very best outputs will likely be eliminated, but so will the very worst.
I don't think that's true. I think it's a binary 0% or near 100% probability of a watermark having been detected; the more changes to the text having been made after the text was output by the LLM and the less leeway the LLM had for probable word choices, the longer the passage necessary to see it.
The "problem" is that seeing the watermark doesn't mean that the person claiming to be the author didn't make extensive changes to the output of the LLM, or that the LLM wasn't simply the final editor of something that the author had put a lot of work into.
> Cognitive surrender.
I don't know what this means. It's just drama. Don't let the LLM write for you and this is not a worry. I'm not worried about the poetry of LLM output being subtly adulterated.
Making blog posts about AI that make it apparent that the tech is going whoosh is a choice.
I couldn't be happier that people are mad about it. To quote Calvin, "nothing helps a bad mood like spreading it around"
"Smart"? Have you read his writings in the last decade? It's all nonsense, which is why I stopped reading circa 2018
I think he’s still generally good on business, UX, and hardware design. That’s all subjective and taste I suppose, but his taste works for me.
On deeper tech stuff, like this utterly nonsensical misunderstanding of watermarks… yeah, classic case of a guy who is smart, and has lost the ability to realize when they’re not knowledgeable in a domain.
>I want any LLM I use to choose the very best, most precise words at every single decision point.
Does the author think he is currently getting T=0 output from Claude? Is he under the impression that T=0 produces the "best" writing?
This entire article just seems so detached from the basics of how LLMs work.
> Does the author think he is currently getting T=0 output from Claude? Is he under the impression that T=0 produces the "best" writing?
No and no. I am not sure I agree with his point but I know he is not ill-informed on either of these points, because I mentioned them to him a couple of days ago.
It didn’t take, apparently.
It’s fully possible I didn’t explain it very well in the first place, but he is making a wider point.
The point I made (quite briefly) is that watermarking is only feasible because for good writing it is necessary to use T>0, or the writing will never explore a more creative choice, and that at T=0 you don’t even need a watermark to spot LLM-generated text.
The point he is making is consistent with this, isn’t it? Either you allow temperature to drive creativity, consistently in a way that can be influenced and analysed, or you adulterate that process for the purposes of meeting a corporate/legal directive, in a way that is proprietary and obscure. These are ethically distinct approaches, and since he disagrees with the EU objective he comes down on one side I guess.
Me, I don’t care about the hypothetical enough.
Not least because I think Claude writes depressingly badly and I doubt any steganographic change will enrage me less.
I think the author is just mad people will be able to detect and filter out their AI slop writing in the future.
This is not it, no. He is not using AI and it is not I think remotely in his nature to surrender that control. He is engaging with this on principle. Again I am not sure I agree with him, but then it’s a hypothetical because I am not going to get an LLM to write for me either.
Translation: No one can ever again use Claude for proofreading their own prose unless they’re willing to risk that the whole thing might be flagged as having been generated by Claude.
I think that was intended, yes.
You know, back in the era when proofreaders were human, I never one met a proofreader who rewrote my text afresh, rather than annotating the text with a pen.
It's still possible to use Claude to proofread - highlight grammatical, flow, structure, logic errors and make simple suggestions for you to pick and choose or adapt as you wish. No watermarking will flag your text. No flaw accusations of LLM authorship will haunt you. All will be fine.
But if you want an LLM to rewrite your text, that's (a) not proofreading, and (b) should be flagged as LLM generated ... because it is.
The question is, when is the "pro writer" version coming that lets you control this behaviour but costs more? Like night follows day, this will happen.
They will need to dodge around the EU requirements but it will probably just come down to an alternative method to watermark or a contractual assurance you won't mis-represent the source of the text.
I'm not sure the quoted statement is true. Proofreading like "point to problems in the text", if you fix the problems yourself and don't copy-paste the solutions given to you, should still be safe, shouldn't it? So, human-written text should not be falsely flagged if you use LLM for proofreading.
And if you copy-paste the answers from LLM, I think it's only fair the end result gets flagged. You're not writing it yourself.
> And if you copy-paste the answers from LLF, I think it's only fair the end result gets flagged. You're not writing it yourself.
I wonder if it would even get flagged in that case, because wouldn't the probability distribution of a token when the LLM is suggesting an edit to your writing be different than the distribution of that token once it is in the context of the text it's editing?
Can't the LLM just generate e.g diffs? Or some other intermediate language. Then the watermark is lost when the translation step is applied.
Rands made this point a few days ago as I recall. Worries about having his tool corrupt his writing during editing, etc.
It is quite just, if you think about it. Human works are copyrighted and protected at the moment of creation. All rights reserved. Yet, LLM outputs are uncopyrightable. Therefore, if Claude or any AI has processed my copyrighted work, the end result is uncopyrightable and in the Public Domain. The public has a right to know: is this a human copyrighted work, an LLM PD work, or is the human falsely claiming authorship in order to retain copyright?
A point of confusion for me, however: is every watermark unique? Is every algorithm for watermarking going to vary amongst models and amongst model versions? Will each model publisher keep this watermarking as a trade secret, that they alone can detect? If so, this can't scale! How do you detect "JoeBob 4.3 LLM" output? By querying every single model's watermark-detector? And if they all work by re-running the model and using tokens anew? That is extraordinarily wasteful.
If a watermark is not self-evident, or universally detectable, then it is no good. Take, for example, US currency. The security measures are published and well known. Any count-out room in retail has a big poster indicating how you can detect authentic US bills. Nobody has to accept non-US currency in the US, and so the only authenticity you need to worry about is your US bills alone. LLM watermarking has none of this in common. Currently sounding like a shitshow, if you ask me.
As I understand it, the current watermarking methods rely on a secret key, making the detection schemes a black box to anyone not in possession of the key. This means organizations like Anthropic are free to make any claim about authorship they want, true or not, and no one can call them on it.
Part of the legislation requires them to make a public AI text detector (ala GPTZero I assume).
Wouldn’t having that be enough to eventually reverse engineer the key?
Not if they designed the algorithm right.
Perhaps LLM outputs are uncopyrightable, but derivative works of copyrighted works are not automatically in the public domain.
That's an intriguing twist, isn't it? It could lead to a tug-of-war.
Working backwards: if it is possible to confirm 100% confidence that a chunk of text is LLM output, then it is "PD until proven otherwise". How can a human reliably assert human authorship of their source text? When all watermark tests fail? Is that proof of humanity now?
If a human proves human authorship, and LLM watermarking tests positive, then is that going to be considered a "derivative work" or not? What if there is an applicable license for the source work, such as "CC-BY-ND" that prohibits derivative works?
This has not been court-tested, and I expect that it will need testing at that level before we can have any assurances.
The watermark, if I'm understanding correctly, only proves that something has been touched at some point by a particular model, not that it was entirely generated by a particular model.
As for it being "uncopyrightable" if it were the output of an LLM, I think computer people are making a very aspie interpretation of a single decision. I think it's more that the LLM (and thereby its owners) cannot itself hold a copyright on its output, that output has to be touched by a person before it is copyrightable. A particular view from the top of a mountain can't be copyrighted, for example, but a photograph of that view can be.
I'm not sure it at all precludes a "robosigning"* sort of situation, where machines generate output, hired temps sign and claim that output, and immediately sign it over to the people who hired them (as a work-for-hire.) Copyright is stupid, artificial law, not logical.
-----
* https://www.mortgageauditsonline.com/what-are-robo-signers/
> The watermark, if I'm understanding correctly, only proves that something has been touched at some point by a particular model, not that it was entirely generated by a particular model.
For the watermark to be detectable, the text needs to be like 75% AI generated.
If you have an LLM “touch” one section of the article, it’s not gonna be detectable.
This article feels slightly incoherent. You want high quality precise writing and to use an LLM to generate it? Feels like those are diametrically opposed
Exactly. The watermark is proportional to how much text is AI generated. Either the AI really just “fixed some typos” (not enough AI content to hide a watermark) or the AI did most of the writing (enough AI content to hide a watermark).
LLMs are already perversions of writing, so what else is new. Oh no, the over-long circumlocution generated by three autocorrects in a trenchcoat might be slightly longer because of this and maybe people will start noticing the subtle rhythms of vaguely peculiar word choices as yet another cue that you are wasting their time with machine-generated wordslop, what a terrible fate. Your long rambling walls of machine-waffling might be 37.05% longer than they need to be instead of the mere 36.58% longer they are now.
This is not meant to be snarky, But almost any writing done by Claude is a perversion of writing.
I honestly can't stand the way Claude writes. This watermark change just makes it scarier.
I moved to Sol for my writing and it is so so much better. But it makes more mistakes. I think they have different ideas of product but it seems OpenAI is going to follow Anthropic’s lead over the next year. I think I am going to put more effort into my writing skills to remove myself from this awful situation
> One of my fundamental problem with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter.
Then why are you using an LLM to write? They're not capable of understanding such nuance. They do pick randomly between two synonymous phrases, they do not use some super smart algorithm to pick the one that sounds the best.
This excuse doesn't hold any water at all - Occam's razor says the author is just super annoyed that his AI writing will be identifiable as AI writing.
> The provider must mandate in their terms-of-service that users not remove the watermarking.
So, you don't own the generated text, and can't use it freely then. What if I copy paste a section, or rewrite a section of text to my liking? What if I rewrite some lines of code that contains the mark?
Security theater, and vague enough to be used as a weapon against who the government wishes.
I hope it's left off for non-EU customers.
> I chose to depend on a private company to express my own thoughts and now I'm mad that I'm not in control of the output
Who could have seen this coming???
It seems fine. I use an LLM to argue with me prior to posting blog posts so that I don't post obvious incorrectness, but the UX element to it is that it constructs notes about various sections of the text and we talk about those. There's no way for the generated text to enter the blog unless I copy-paste it and I'm not going to do that because the entire point is for me to write it.
At the point that you're generating entire volumes of text from Claude you're not really trying to be a sophisticated writer. I don't see how it's going to hurt for it to choose random related words.
Some here are arguing that mechanisms used by LLM providers already derail the goal of "the very best, most precise words at every single decision point", therefore the author is misguided.
The author has expressed a preference. Assume that there is a sequence of tokens, such that it is considered the absolute best by the author. This particular method of watermarking makes it less likely to generate that sequence, by definition.
I feel their argument would have been clearer and stronger if they had spent more time exploring the alternatives, and whether these alternatives would be just as effective. It is trivially easy to remove invisible tokens.
Like it or not, there is a public good to being able to identify AI generated content, and a small degredation in quality is tolerable in my opinion.
I don't think anybody has to worry about this issue though. Manual writing, coding, and proof reading continues to be an option. Where AI output is nothing to be ashamed of, the tools are available. For everyone else, there will be LLM providers that ignore EU law.
If the author has preferences on their "own writing" that conflict with Anthropic's, then they should actually write it themselves rather than paying Anthropic to do it. Private companies don't owe you anything, even less so when they're beholden to laws in foreign jurisdictions.
>Assume that there is a sequence of tokens, such that it is considered the absolute best by the author
You can't assume that because if that was the case he'd already know what sentence to write, because that's what that means.
The notion of a best sentence requires a final cause, an end to write to. By their very nature that's not how LLMs work, so you can't 'degrade' them on that front. They can't lose a property they didn't have.
Claude's writing was already easy to recognize. The fact that Anthropic complied without complaint makes me wonder if they already watermark their outputs and used the opportunity to create goodwill. Presumably they want to avoid training their new model on text generated by the previous model, so they have reasons to be able to recognize AI-generated text.
I dont see how there would be remotely enough entropy in most model outputs for this to be close to feasible with any kind of accuracy.
Either they false positve on pretty much everything ever written, or the chances of catching a true positive is so low as to be useless.
Basically Cinavia for text, and that often falls over and is easy to remove even when there is megabytes of data streaming over a long period of time rather than 2 or 3 bits per wall of text, let alone what most people use claude for, when there is a strict dictionary and other tight output constraints.
> I want any LLM I use to choose the very best, most precise words at every single decision point.
Try running an llm like qwen 3.8 27B in Q8 locally with an intentionally very low temperature setting, it will write like a caveman crossed with a robot. You may find that an extremely literal output does not look pleasant to read for humans.
That is not what that means. Generally, precise word choice requires more than autocomplete. Larger models simulate this with hidden layers.
Excessively precise word choice does not result in something that looks like content written by, or palatable to humans. It looks like you gave a high school 12 grade student a science paper and told them to apply a thesaurus to at least one word in every sentence and replace it with something else.
There is a difference between precise word choice and concise word choice. You can be precisely accessible the same as you can be concisely terse.
I was initially surprised that Gruber was so invested in the "quality" of AI-generated text, which in my mind is an oxymoron. But really, Gruber's interest here is with the EU. This forms part of his ongoing attacks on the EU, all because they have been forcing Apple to align with regulations.
Watermarks are garbage because they may embed account id, IP address and deanonimize you. That's why we should be using open-weights LLM whenever possible.
This is the first post I've seen mention it. How traceable are the embedded codes?
I'd encourage reading this paper, and literature on scaling laws in autoregressive models: https://arxiv.org/abs/2303.11156
Total variation distance has been measured to decrease as you scale a model, and that is the primary mechanism "watermarking" as discussed in the Anthropic announcement relies on. It becomes more difficult to reliably detect text as a fixed sample count without tweaking the distribution further. Either way, it's a minor problem that will be addressed over time, compared to the issue of who can detect this without guessing or developing their own sets: providers not releasing a way to detect any such watermarks without going through them makes this entire approach hostile to the public. The EU regulation on this subject is interesting, although again most certainly not the primary driver for these practices:
"1.1.2: Signatories will ensure that AI-generated or manipulated content is marked with an imperceptible watermark, with the exception of very short text. For free-form text longer than 200 tokens, watermarking still needs to be applied, even though it may have lower reliability compared to that of watermarking very long text"
A proper, effective and useful law would have required providers to regularly release datasets to run your own verification on any text released within a fixed interval of time, presumably once out of rotation. Instead, it only talks about exposing an user interface going through their own services:
"Signatories will ensure access to their detection solution through a user interface appropriate for the audience of end-users that may eventually be exposed to the content generated or manipulated by their AI system. [...] Any restriction to the access will be limited in time until more reliable and robust detection mechanisms have emerged and have been adopted as the state of the art for detection mechanisms for the watermarking of free-form text evolves."
Most interestingly, in line with the EU's mass-surveillance program, an alternative solution to watermarking where it may not be sufficient is also suggested, although only optional for now:
"Where appropriate and taking into account potential trade-offs related to privacy and security, as well as scalability challenges and costs, Signatories may implement as an optional supplementary measure fingerprinting or logging solutions for AI-generated or manipulated content which allow for checking whether content has been generated or manipulated by their AI system. For example, direct logging may be appropriate for text content, whereas fingerprinting approaches may be preferable for audio and visual content."
There is no coherent position in which the watermarking is a perversion of writing but AI writing as a whole is not a worse one.
Watermarking will be one more nail in the coffin of proprietary models if the world is so fortunate.
Reminds me of printer tracking dots.
https://en.wikipedia.org/wiki/Printer_tracking_dots
It's good to be the King.
And what I mean by that is that companies that are at the top tend to make anti customer decisions because they have lost the concept that pleasing customers matters as priority one.
it serves to show just how little regard the people behind these generated-text fingerprinting schemes have for the actual craft of writing.
LLMs have never been the place I've thought to expect any commitment to the craft of writing, to be fair.
I think it's pretty dishonest of Anthropic to frame their watermark as EU regulation compliance. The EU regulation, from my understanding, requires AI content to be labeled for human viewers. In the meanwhile the Anthropic new release on the watermark says this.
> The difference between watermarked and un-watermarked text will not be distinguishable to readers
https://www.anthropic.com/news/claude-text-watermark
Which is to say, it does not actually meet the EU AI act requirements which require transparency to humans. Not to mention that if the detection requires access to the base models, it makes anthropic the only entity who gets the say on if a piece of text comes out of Claude. Anthropic is both the player and the referee here.
If there is one takeaway you should have from this fiasco it is that you should be wary of using tools that doesn't serve your needs and your needs only.
> The EU regulation, from my understanding, requires AI content to be labeled for human viewers.
How would that work? Claude appending " written by AI" to each of its messages? That would both be impracticalc and useless.
Lots of faux outrage, rambling and hyperbole here from Gruber.
“Absurdly and insultingly”? Come on…
Reminder: your favorite distilled model does not treat you, the customer, as an adversary and mess with your output.... May the free market win.
I don’t understand how this works for anything but prose. Is that the point? In any code or structured output, there just isn’t the flexibility, and depending on how the user requests the output be constrained there is even less (“answer only True or False”). So is it just chat responses? If I ask the API to tell me a story about Alice and Bob then it watermarks it, but when I ask it some implausibly constrained thing like write a story about Alice and Bob with each word starting in rotation with the letters alicebob, does it try to do so and hope there are roughly équiprobable tokens regularly?
Yes, and it says that in https://www.anthropic.com/news/claude-text-watermark
I should have read that, it’s actually quite reasonable and I don’t really understand the objections in TFA having read it.
> One of my fundamental problem with this is that no two synonyms carry the exact same meaning. “He leaped at the chance” and “He jumped at the opportunity” are very similar sentences expressing the same general sentiment, but they are not the same. The exact words we choose when writing matter.
Doesn’t make sense at all in light of the actual approach, they’re just choosing a different RNG. It’s not like they’re corrupting it by flipping words.
Should add I don’t support the watermarking and requiring it is idiotic.
I don't understand Gruber's points either, I wonder if there is some fundamental technical misunderstanding. Does he think that the logits should be sampled from in a "pure" manner without introducing any other bias? Does he know that there's already a sampling temperature, and that most providers have probably moved on to sampling strategies other than top-k? Does he know that the word choices have already been altered irreversibly during RLHF which is how you get the obvious Claudism like "load bearing" and "seams"?
Perhaps it would be useful to publish examples of samples with/without watermark. I'd suspect that the variability from simply sampling repeated times would dwarf any semantic differences you'd detect with the watermark.
I think Anthropic should have put all the info into one blog post. Splitting it up is really confusing people.
> In any code or structured output, there just isn’t the flexibility
Variable name perversion incoming...
gruber is really out of his element with ai commentary, I fully support the general skepticism but he's seemingly arguing against something he doesn't quite grasp
I'll be honest, who fucking cares? Why would you use AI to write for you and then complain that people know AI wrote the code?? If you know people wouldn't like it, why even try!?
The objection seems to be that Claude will always write worse prose than a human writer, even if the writing Claude generates is understandable.
Yeah, John. We're all OK with that.
Anthropic should just pull out of the EU at this point. Europeans who really want to use it can VPN, and if they did they wouldn’t be able to hide behind their various comical tech laws.
Nanny state nonsense indeed.
As a non American/non European resident all I see from the Europeans are 0 contributions to software progress at any large scale while they surely do a lot of crying and huffing and puffing and demanding. Lots of complaining and rule making but not a lot of creating is a bad look.
> the only acceptable answer for why an LLM should choose bananas instead of pineapple (or coconut, or guava, or papaya...) is that it has determined that it’s the best fit for the intended meaning, tone, and sentiment of the text.
It already fails. It randomly picks between close candidates. To help fool people into believing in intelligence claim, I guess.
"Anthropic's . . . Claude is a Perversion of Writing."
FITFY.
I have no sympathy for writers whining about what the AI is doing to 'their' writing. It's only your writing when you write it. There's any easy way to avoid this: don't fucking use it. Use you own brain.
I keep seeing an irritating misconception in this space, which is that the alternatives chosen by these algorithms are supposed to mean the same things as what they're displacing. That's not true, and not how LLM generation works. Complaints that two different choices don't mean the same thing miss the entire point.
Gruber has a ridiculous knee-jerk response to anything the EU does, so hardly a surprise he didn't come to the table with a sober facts-based response.
Not telling someone you used AI is a perversion of writing. Also agree that an AI proofreader should not claim authorship, but in most other cases, the AI is not reading your mind, it's only watermarking its own usage, and we kind of need more of that.