this post was submitted on 21 May 2024
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This is a really good science communication article, it describes their work in clear terms (finding structures that relate to abstract concepts, seeing when they are activated and how strengthening and weaking them modifies outputs) and goes into the implications for it. I'm probably going to save this link as a rebuttal for the people who claim LLMs just predict the next word and have no concepts embedded in them.
I doubt that anyone saying that LLM are calculating next word solely based on previous sequence. It's still statistics, regardless of complexity.
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.
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.