[mips_avatar]

From the model card: "the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning" aka they will take your ML research code and inject bugs into it until it breaks using a LORA (or some other form of PEFT)

[sciencejerk]

Are they trying to fight back against model distillation?

[bee_rider]

“Limit effectiveness” could mean introducing performance degradation in your code. Which is arguably some sort of performance bug (I mean, ML codes are supposed to be high performance so I’d call unnecessary degradation a bug), but it could be borderline.

[rurban]

No, it is just a prominent "Cyber Security threat detected" blocker, with a button to appeal. I appealed because my work had nothing to do with neither cyber nor security, but the appeal was auto-closed. So no more Claude for this work.

[nomel]

Thanks, I thought maybe I missed something. That's an interesting way to interpret that.

[mips_avatar]

Anthropic is trying to hide bad behavior by being vague, it's important to not be vague when calling it out.

[nomel]

I'm of the opinion that removing guardrails is how you force regulation. What's your opinion on the balance?

[dannyw]

They have all transcripts for at least 30 days. The problem is that (as anyone who used Fable can attest) their classifiers are extremely sensitive and catch tons of innocent queries.

Imagine being a data scientist or MLE training a small classifier model. How do you know you won’t get steering vectors or a PEFT applied?

[nomel]

Since your answer isn't direct, I'm having a little trouble interpreting it.

Are you saying they should relax guardrails since they have 30 days to know if you produced something bad? If that is what you're saying, then I suspect they chose their current path to prevent, since you can't un-produce. Producing is what would cause regulations/PR problems.

[dannyw]

Sorry, I’m specifically referring to the silent degradation of the model to “limit frontier LLM development”. From the description, it appears to encapsulate far more than frontier LLM development, but general ML research and development too.

Those cases are never bad for the world firstly, and a broad coverage of ML work is even more damaging.

My proposal would be (1) don’t degrade models, with 30D retention I’m sure they can do a reasonable job at banning deepseek or whatever, or (2) surface user facing refusals instead of silently degrading ML work.

[mips_avatar]

They’re not safety guardrails they’re anthropic doesn’t like anyone who isn’t anthropic working on AI rails

[giancarlostoro]

PEFT is a library, one of its capabilities is to produce LoRAs.

See:

https://heidloff.net/article/efficient-fine-tuning-lora/

[adw]

It's just an acronym, "parameter-efficient fine tuning". LoRA is one method, prefix tuning is another, there are more.