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This is precisely the wrong way to think about LLMs. LLMs are never going to have fact retrieval as a strength. Transformer models don't store their training d
by lukev 1y ago
This is precisely the wrong way to think about LLMs.
LLMs are never going to have fact retrieval as a strength. Transformer models don't store their training data: they are categorically incapable of telling you where a fact comes from. They also cannot escape the laws of information theory: storing information requires bits. Storing all the world's obscure information requires quite a lot of bits.
What we want out of LLMs is large context, strong reasoning and linguistic facility. Couple these with tool use and data retrieval, and you can start to build useful systems.
From this point of view, the more of a model's total weight footprint is dedicated to "fact storage", the less desirable it is.
- superconduct123 1y agoHow can you reason correctly if you don't have any way to know which facts are real vs hallucinated?
- futureshock 1y agoI think that sounds very reasonable, but unfortunately these models don’t know what they know and don’t. A small model that knew the exact limits of its knowledge would be very powerful.
- energy123 1y agoHallucinations have characteristics in interpretability studies. That's a foothold into reducing them. They still won't store much information, but it could mean they're better able to know what they don't know.
- CrackerNews 1y agoWhat are the large context, strong reasoning, and linguistic facility for if there aren't facts underpinning them? Is a priori wholly independent of a posteriori? Is it practical for the former to be wholly independent of the latter?