I’ve been saying this for about a year since seeing the Othello GPT research, but it’s nice to see more minds changing as the research builds up.
Edit: Because people aren’t actually reading and just commenting based on the headline, a relevant part of the article:
New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.
This theoretical approach, which provides a mathematically provable argument for how and why an LLM can develop so many abilities, has convinced experts like Hinton, and others. And when Arora and his team tested some of its predictions, they found that these models behaved almost exactly as expected. From all accounts, they’ve made a strong case that the largest LLMs are not just parroting what they’ve seen before.
“[They] cannot be just mimicking what has been seen in the training data,” said Sébastien Bubeck, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight.”
No, but I have learned over the years that when you see multiple papers discovering similar things at odds with the held consensus and see some even independently replicated that there’s usually more than just smoke.
The paper was titled “Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models.” Quanta, while a Pulizer winner in 2022 for explanatory reporting, is after all a publisher not a research institution. Though I dispute your issues with the headline as it’s in line with similar article headlines such as “Bees understand the concept of zero”.
You wouldn’t be the only person looking at it through that lens. It was more popular a few years ago I think, and hasn’t really caught on for LLMs vs other ML approaches and here it strikes me a bit like those with hammers looking for nails - the degree to which there’s functional overlaps in network introspection such as the linked Anthropic work suggests to me that the internalized delineations are a bit fuzzier than would cleanly map onto a category theory view - but it’s possible that as time goes on that it gets some research wins assuming it can come up with testable predictions that are successful. But it’s more of a ‘how’ than a ‘what’ question - whether a network understands abstract concepts tangental to language it is trained on and develops world models (an idea that would have been laughed out of the room just three years ago by any serious researchers despite your impression) using something that can be explained through category theory or through another interpretation, the result is arguably the more important finding than the interpretation of the means.
It seems like you may be more committed to arguing the semantics and nuances of the tree in front of you than discussing the forest - that’s fine, it’s just not that interesting to me in turn.
To hijack your analogy its more akin to me stating a tree is a plant and you saying “So are these” pointing at a forest of plastic Christmas trees.
I’m pretty curious why you imagine you have so many downvotes?
Because laypeople are very committed to a certain perspective of LLMs right now.
You should see the downvotes I got a year or two ago explaining immunology research to antivaxxers.
Have you ever considered you might be the laypeople?
Equating a debate about the origin of understanding to antivaxxers…
You argue like a Trump supporter.