alt Hacker News> “Google Translate is amazing, but it has taught us essentially nothing about linguistics,” Strogatz said. “We don’t understand why it works. It just works. That’s the Twilight Zone. It is knowledge without understanding.”
This is not true, and it is the crux of an enormous debate we're now having across many, many fields.
Classical Chomskyan syntax (which models sentences as parse trees) has been enormously, astonishingly predictive of the rich structure in real-world human language. But we are quick to forget that we invented concepts like "words" and "noun phrases" specifically to help us analyze and predict things about human language. What LLMs and modern MT shows, definitively, is that there is another, totally separate way to describe these phenomena, which is both vastly less descriptive, and also vastly more predictive.
That these appear to trade-off is unfortunate, but it is NOT the case that we "don't understand why it works." We absolutely understand why it works, if we didn't understand we would not be able to improve these systems. What we do not have is a clean, descriptive theory that boils this down to something that is formally specifiable and analyzable. But those are different things, and this is the root of the disconnect between many communities working in and around these spaces (linguists vs computational linguists; mathematicians vs computational mathematicians; mech interp people vs empiricists).
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I think they're referring to the fact that neural networks are more or less black boxes. Their internals are blurry and not explainable in the same way that traditional linguistics tries to explain things - with rules.
Of course, ultimately that's because language itself doesn't follow simple rules like linguists would prefer. They're more like guidelines.
"We absolutely understand why it works, if we didn't understand we would not be able to improve these systems" - Understanding how to improve something does not require understanding how it works.
Growing a plant does not imply understanding soil minerals or photosynthesis. Training a dog does not imply understanding it's brain. Using LLMs to improve a codebase does not imply understanding C++. Improving a PC with new parts, does not imply understanding RAM.
You can understand abstractions about the system, without understanding the system. Without gradient descent or other forms of automatic optimisation, do you think humans could build something as capable as GPT-1?