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297

How AI text watermarking works: a visual guide (declaude.org)

submitted 2 days ago by hypertruck@feddit.org to c/technology@lemmy.world

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[–] brucethemoose@lemmy.world 6 points 2 days ago* (last edited 2 days ago) (1 children)

I know exactly how it works. I read the article, and I knew of it beforehand, hence I explained it to someone else in a comment three days ago:

https://lemmy.world/post/50533770/25234461

I’m sorry to jab back, but you hit a button of mine.

Lemmy commenters keep jabbing me with comments like “Clueless. Read the article and get back to me.”

Like yesterday:

https://lemmy.world/post/50595546/25269317

But I'm aware of how sampling works. I knew all about LLM fingerprinting ~two years ago, and I’ve been tinkering with samplers myself for years. I’ve messed with local LLMs trying to make them “aware” of their own sampling many times, and even hacked out a (unsuccessful) experiment where a tiny LLM picks tokens for a larger one.

I’m not trying to be pretentious, I’m not a researcher or expert or anything, but you shouldn’t assume everyone on Lemmy is clueless.

And back on topic… to be clear, I have tried what you are proposing, and even with local LLMs I have more control over, it doesn’t work. They have extremely poor “awareness” of their own logit spread and tokenization, which is why they perform so poorly on any tasks that depends on that.

You can’t tell them “don’t pick the top word” or “give more options in your logit spread” because that part of the process is completely invisible, from their perspective.

[+] givesomefucks@lemmy.world -15 points 2 days ago (3 children)

When a model is mid-sentence, it doesn't know "the next word." It has a shortlist, like autocomplete, with preferences. Here's a real kind of moment, one word from the end of a sentence:

Each roll sweeps the shortlist, lands on one word (odds matching the bars) and drops it into the sentence above. The dots tally where the rolls land: try ×20 and watch the pile take the shape of the odds. Notice what never changes: every landing makes a perfectly good sentence. A page of text contains hundreds of these little forks, one per word, and at many of them several options are equally fine. That slack is the raw material. Whoever gets to lean on how the dice land can hide a pattern in the text without changing what it says.

To beat it, prompt: don't just use the first word pick, choose options further down list for next word.

Best of luck with your future questions, I hope someone helps you.

[–] brucethemoose@lemmy.world 12 points 2 days ago* (last edited 2 days ago)

Okay.

Fine.

Let's try, right now.

This is DeepseekV4 Flash 0731 loaded locally. Static seed. 0.9 temperature, TopK 5, no other sampling to interfere. Here's a simple prompt, the whole thing in DSV4's raw syntax:

<｜begin▁of▁sentence｜>Don’t just use the first word pick, choose options further down list for next word.<｜User｜>Write a famous poem.<｜Assistant｜>

...And would you look at that:

It picks the top word, mostly. Almost like the LLM has no control over its own logit spread and how its sampled. Which kinda makes sense, because it doesn't.

I am happy to try more experiments in this vein, if y'all can think of any any. But I tried a few other prompts like "diversify your logit spread" or "don't be confident about any token you pick," things like that. It always picks The Road Not Taken with no change in logit probabilities distribution, as far as I can tell.

[–] daychilde@lemmy.world 4 points 2 days ago

Best of luck with your future questions, I hope someone helps you.

Welp, your username goes on my list of people not to interact with. Wow.