I'm not saying that pre-trained only models are useless. They've clearly extracted a ton of knowledge from the corpus. The interface may seem strange because it's not what we're accustom to but they still prove valuable. Code completion models, for example, are just LLMs that have pre-trained exclusively on code. They work very well despite their simplicity because... the model has extracted the signal from the noise.

You have a strange definition of "signal" and "noise".

Code completion models can be useful because they output the most probable chain of tokens given a specific input, same as any LLM. There is no "signal" there besides probability. Besides, even those models are fine-tuned to follow best practices, specific language idioms, etc.

When we talk about "signal" in the context of general knowledge we refer to information that is meaningful and accurate for a specific context and input. So that if the user asks proof of the Earth being flat, the model doesn't give them false information from a random blog. Of course, LLMs still fall short at this, but post-training is crucial to boost the signal away from the noise. There's nothing inherent in the way LLMs work to make them do this. It is entirely based on the quality of the training data.