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An LLM has no concept of truth or falsehood, or facts, or logic, or anything else that matters for users. All it does is produce text that closely resembles (st
by SrslyJosh 3y ago
An LLM has no concept of truth or falsehood, or facts, or logic, or anything else that matters for users. All it does is produce text that closely resembles (statistically speaking) the material it was trained on.
Training the model on "accurate" data isn't going to change the fact that you're operating at a completely different level of abstraction from the model -- one that it can't operate at, because that's simply not how it works.
This is the same problem that image models have. A human artist works with shapes, values, textures, colors, etc. An image model works with pixels, with zero higher-level abstractions or reasoning informing its output.
- IIAOPSW 3y agoI see no reason why the image model couldn't work in a space of shapes and textures which are then mapped to pixels after the fact. Or even just leave its output in a raw vector based format. You could pick a different basis to work in. Though I agree there is something ontological missing. All of these are flat basis. There's nothing spanning the recursive dimension. It can't draw a picture within a picture within a picture...to some specified depth n because it does not work with abstractions of "objects". The joke of Rene Margrit's "treachery of images" is lost on the AI.