With respect to Chollet’s definition (the youtube link):
I agree with many of Chollet’s points, and the third and fourth items in my list are intended to get at those.
I do find Chollet a bit frustrating in some ways, because he seems somewhat inconsistent about what he’s saying. Sometimes he seems to be saying that LLMs are fundamentally incapable of handling real novelty, and we need something very new and different. Other times he seems to be saying it’s a matter of degree: that LLMs are doing the right things but are just sample-inefficient and don’t have a good way to incorporate new information. I imagine that he has a single coherent view internally and just isn’t expressing it as clearly as I’d like, although of course I can’t know.
I think part of the challenge around all of this is that (AFAIK but I would love to be corrected) we don’t have a good way to identify what’s in and out of distribution for models trained on such diverse data, and don’t have a clear understanding of what constitutes novelty in a problem.
I agree with your frustrations, I think his views are somewhat inconsistent and confusing. But I also find my own understanding to be a bit confused and in need of better sources.
I do think the discussion François has in this interview is interesting. He talks about the ways people have tried to apply LLMs to ARC, and I think he makes some good points about the strengths and shortcomings of LLMs on tasks like this.
But I also find my own understanding to be a bit confused and in need of better sources.
Mine too, for sure.
And agreed, Chollet’s points are really interesting. As much as I’m sometimes frustrated with him, I think that ARC-AGI and his willingness to (get someone to) stake substantial money on it has done a lot to clarify the discourse around LLM generality, and also makes it harder for people to move the goalposts and then claim they were never moved).
With respect to Chollet’s definition (the youtube link):
I agree with many of Chollet’s points, and the third and fourth items in my list are intended to get at those.
I do find Chollet a bit frustrating in some ways, because he seems somewhat inconsistent about what he’s saying. Sometimes he seems to be saying that LLMs are fundamentally incapable of handling real novelty, and we need something very new and different. Other times he seems to be saying it’s a matter of degree: that LLMs are doing the right things but are just sample-inefficient and don’t have a good way to incorporate new information. I imagine that he has a single coherent view internally and just isn’t expressing it as clearly as I’d like, although of course I can’t know.
I think part of the challenge around all of this is that (AFAIK but I would love to be corrected) we don’t have a good way to identify what’s in and out of distribution for models trained on such diverse data, and don’t have a clear understanding of what constitutes novelty in a problem.
I agree with your frustrations, I think his views are somewhat inconsistent and confusing. But I also find my own understanding to be a bit confused and in need of better sources.
I do think the discussion François has in this interview is interesting. He talks about the ways people have tried to apply LLMs to ARC, and I think he makes some good points about the strengths and shortcomings of LLMs on tasks like this.
Mine too, for sure.
And agreed, Chollet’s points are really interesting. As much as I’m sometimes frustrated with him, I think that ARC-AGI and his willingness to (get someone to) stake substantial money on it has done a lot to clarify the discourse around LLM generality, and also makes it harder for people to move the goalposts and then claim they were never moved).