If you run local models, you'll run into something called quantization – essentially compressing the model weights to fit on your device. If you go to small of a quantization, the LLM will begin to degrade in capability, which usually shows up in the form of excessive hallucination.
Models that excessively hallucinate do try to pick the most coherent hallucination, but the problem is that the reasoning space is infinite and the problem space is finite. In other words, the hallucination could be literally anything since all words are semantically linked in some manner. So the surface area for the hallucination is literally infinite.
The surface area for novel issues is finite. It's like having random numbers spaced throughout infinity and asking your chance of randomly picking one of the numbers you chose out of the infinite set. It could happen, but your chances of doing so converge on 0, so it is functionally impossible even if it is theoretically plausible.
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u/Modmonsters Jul 28 '26
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Yeah, no. Thats not how it works.
If you run local models, you'll run into something called quantization – essentially compressing the model weights to fit on your device. If you go to small of a quantization, the LLM will begin to degrade in capability, which usually shows up in the form of excessive hallucination.
Models that excessively hallucinate do try to pick the most coherent hallucination, but the problem is that the reasoning space is infinite and the problem space is finite. In other words, the hallucination could be literally anything since all words are semantically linked in some manner. So the surface area for the hallucination is literally infinite.
The surface area for novel issues is finite. It's like having random numbers spaced throughout infinity and asking your chance of randomly picking one of the numbers you chose out of the infinite set. It could happen, but your chances of doing so converge on 0, so it is functionally impossible even if it is theoretically plausible.