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Published · transcript-backed

Nicholas Carlini: belief

29 Aug 2024 Latent Space Why you should write your own LLM benchmarks — with Nicholas Carlini, Google DeepMind

“I think probably this is a thing that if you really cared about this task being solved well, you would train a model for.”

— Nicholas Carlini

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Speaker
Nicholas Carlini
Attribution
Verified speaker
Claim type
belief
Recorded
29 Aug 2024
Publisher
Latent Space

Transcript context

…in something. And it was doing it correctly for like 99% of cases. And I found a few UU encoded things where it couldn't figure out this was UU encoding, not base 64. OK. This is not important. I just was curious if it could do it. And so I put this as a thing. I think probably this is a thing that if you really cared about this task being solved well, you would train a model for. But again, this is one of these kinds of tasks that this was some dumb project that no one's going to care about. I just wanted to see if I could do it. If the model was good enough that it gets me 90% of the way there, good, like done. I figured it out. Like I can sort of have fun for a couple hours and then move on. And that's all I want. I was not like, if I ever had to train a thing for this, I was not going to do it. And so it did well enough for me that I could move on. It does give me an idea for adversarial examples inside of a benchmark that are basically canaries for overtraining on the benchmark. Typically, right now, benchmarks have canary strings. If you ask it to repeat back the string and it does, then it's trained on it. But, you know, it's easy to filter out those things. But the benchmarks, you put in some things, some questions that are intentionally wrong. And if it gives you the intentionally wrong answer, then you know it's. Yeah, there are actually…

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