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- cross-posted to:
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I often see a lot of people with outdated understanding of modern LLMs.
This is probably the best interpretability research to date, by the leading interpretability research team.
It’s worth a read if you want a peek behind the curtain on modern models.
Saying that it’s “statistics” is, at best, unhelpful. It conveys no useful information. At worst, it’s misleading. What goes on with neural nets has very little to do with what one learns in a stats course.
Most people don’t know what Bayesian statistics are so you could say most people don’t really get how machine learning works in general anyway. It’s not misleading though as it perfectly sets expectations on what you’re getting as output. It’s much more healthy to general understanding of AI than anthropomorphizing very inflexible and limited models achieved thanks to technology that is seemingly in a plateau.
I would not expect almost human-like conversation on being told that is just statistics. I’d expect something like the old Markov chain jobs. What kind of knowledge leads you to have higher expectations?
Also, how does Bayesian statistics enter into this?
ELIZA from 1966 was enough to convince people that computer program they were talking to was human. People are now being sold on getting answers to their questions via natural language prompts and those answers are pretty much plausibly sounding sentences that happen to be right sometimes due to probability calculations.
Bayesian statistics is very different from what’s being taught up until high school (at least here) and is foundational to earlier machine learning applications like spam filters. It’s hard to imagine understanding what LLMs do without basics.
Those aren’t the basics, though. That’s how saying it’s statistics is misleading. A Bayesian network is not a neural network.
Yeah, it’s about as useful as saying that all of science is “just statistics”. Which like, in a literal way, it’s true. But science is still what forms the foundation of our entire civilization and base of knowledge.
Knowing that a blood pressure drug works is “just statistics”, but you still take it if your blood pressure is high.
Yes, that’s a valid comparison. It’s worse with neural nets, though. Much of machine learning is literally applied statistics. That is, a program is written that applies statistical methods to data and then adjusts its behavior. So, saying that it’s statistics has the potential to really send people down the wrong track. Many of the “human hallucinations” about AIs result from confusion about this.