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You can't prove LLMs can build theories like humans can, because we can effectively prove they can't. Most code bases do not fit in a context window. And any "t
by jerf 1y ago
You can't prove LLMs can build theories like humans can, because we can effectively prove they can't. Most code bases do not fit in a context window. And any "theory" an LLM might build about a code base, analogously to the recent reasoning models, itself has to carve a chunk out of the context window, at what would have to be a fairly non-trivial percentage expansion of tokens versus the underlying code base, and there's already not enough tokens. There's no way that is big enough to build a theory of a code base.
"Building a theory" is something I expect the next generation of AIs to do, something that has some sort of memory that isn't just a bigger and bigger context window. As I often observe, LLMs != AI. The fact that an LLM by its nature can't build a model of a program doesn't mean that some future AI can't.
- imtringued 1y agoThis is correct. The model context is a form of short term memory. It turns out LLMs have an incredible short term memory, but simultaneously that is all they have. What I personally find perplexing is that we are still stuck at having a single context window. Everyone knows that turing machines with two tapes require significantly fewer operations than a single tape turning machine that needs to simulate multiple tapes. The reasoning stuff should be thrown into a separate context window that is not subject to training loss (only the final answer).
- fouc 1y agoOr have at least 2 models. Each with their own dedicated context.