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Comment by egeozcan | Hacker News Reader

I'd kindly suggest that we could also stop calling them "distillation attacks".

Agreed. I think when it comes to light that Claude has been known to say “I’m DeepSeek” that everyone has had their hand in that cookie jar. Moreover, paying for API calls hardly seems like an attack; ToS violation to be certain but not in the same category of law as criminal activity like hacking.

wait, is there evidence of this? I've not observed it. It sounds like the kind of thing that I want to be true because it would be hilarious but that makes me suspicious.

LLMs can't reliably answer what they are (w/o getting that info from system prompt/tool call/etc), so yea.

https://xcancel.com/teortaxesTex/status/2026130112685416881

I think I've seen same happening with some European languages as well.

Ask it in Chinese

Agreed, "distilled variants" might be more suitable.

Calling them variants is also inaccurate when the pretrained base and architecture are completely different.

Why on earth wouldn't you? It's clearly a forcible, aggressive, non-consensual attempt to take something. That's an attack in any other terms. It's totally fair if you approve of the attack, and want the attack to succeed. But your preference doesn't stop it from being what it is.

> It's clearly a forcible, aggressive, non-consensual attempt to take something

Distillers are not taking anything, they are just making their model learn from better ones - isn't that the whole AI training doesn't violate IP argument?

They just aren't using the tool in compliance with the terms of service. Anthropic could ban them or take them to court maybe. Not an attack still.

Fine, then let's refer to the initial data collection/training as 'compilation attacks' going forward.

...or maybe we stop defaulting to adversarial paradigms for every conceivable situation.

"They are using and paying for an API I am selling...But they save the data and use it for something I don't like! I'm being attacked."

We could call it distillation learning

A model is teaching another here

Everyone wins

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