I think the idea is that the same abilities may emerge with much less training if, for example, the models are allowed to explore virtual worlds rather than just learning from static/passive input
I think the idea is that the same abilities may emerge with much less training/compute if, for example, the models are allowed to explore virtual worlds rather than just learning from static/passive input
Yes, as Gary mentions, similar abilities but with broader generalization can emerge with less training.
Emergence is treated as some kind of unexplainable magic right now because we don't understand all the mechanisms. One could say that 'better stability emerged in larger blimps'. We don't say that because we know how/why it happens. I think we will figure out some of the reasons for the emergent capabilities.
I feel like we have a conceptual-level understanding of what's happening w/ these models (though judging by a lot of the discourse, it seems like a (very?) counter-intuitive idea: In the course of predicting words, a system ends up learning world models (outlines of physical laws, topics of conversation, a ton of common sense reasoning) -- not because we necessarily talk about these things but because inferring such models helps make sense of the observed language and therefore lower prediction error. Humans too do this when we learn, but arguably not to the same extent and instead we learn much more by actively trying things out and observing the consequences.
I somewhat disagree about that. I don't think the model infers physical laws. Those are all expressed in words online, and sophisticated word filling-in using statistics is good enough to look like physical reasoning. (Not downplaying statistics in any way...I think it is cool). I don't think physical laws are inferable from words, but ways of filling in words describing physical laws can be learned from text describing physical laws.
What the model has is interesting generalization patterns for sequences, and when applied over language it looks smart and looks like it is inferring things. It learns small automata that can mix slots and content, because the architecture has biases for forcing it that way. I will write more about this soon...hopefully as a paper.
Interested to hear your reasoning! In particular, whether you feel the model architecture aren't able to theoretically store these sorts of models or whether they just aren't achievable to be built using gradient descent etc.
So good! I learned so much though now will spend rest of my night going down a rabbit hole on dirigibles :)
Question though - isn't there a distinction in that, in scaling LLMs, you get emergent effects (like induction heads etc). Does the analogy hold?
I think the idea is that the same abilities may emerge with much less training if, for example, the models are allowed to explore virtual worlds rather than just learning from static/passive input
I think the idea is that the same abilities may emerge with much less training/compute if, for example, the models are allowed to explore virtual worlds rather than just learning from static/passive input
Yes, as Gary mentions, similar abilities but with broader generalization can emerge with less training.
Emergence is treated as some kind of unexplainable magic right now because we don't understand all the mechanisms. One could say that 'better stability emerged in larger blimps'. We don't say that because we know how/why it happens. I think we will figure out some of the reasons for the emergent capabilities.
I feel like we have a conceptual-level understanding of what's happening w/ these models (though judging by a lot of the discourse, it seems like a (very?) counter-intuitive idea: In the course of predicting words, a system ends up learning world models (outlines of physical laws, topics of conversation, a ton of common sense reasoning) -- not because we necessarily talk about these things but because inferring such models helps make sense of the observed language and therefore lower prediction error. Humans too do this when we learn, but arguably not to the same extent and instead we learn much more by actively trying things out and observing the consequences.
I somewhat disagree about that. I don't think the model infers physical laws. Those are all expressed in words online, and sophisticated word filling-in using statistics is good enough to look like physical reasoning. (Not downplaying statistics in any way...I think it is cool). I don't think physical laws are inferable from words, but ways of filling in words describing physical laws can be learned from text describing physical laws.
What the model has is interesting generalization patterns for sequences, and when applied over language it looks smart and looks like it is inferring things. It learns small automata that can mix slots and content, because the architecture has biases for forcing it that way. I will write more about this soon...hopefully as a paper.
Interested to hear your reasoning! In particular, whether you feel the model architecture aren't able to theoretically store these sorts of models or whether they just aren't achievable to be built using gradient descent etc.