A lot of the touted "fundamental limitations of LLMs" are less "fundamental" and more "you're training them wrong".

So there are improvements version to version - from both increases in raw model capabilities and better training methods being used.

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ijk 3 days ago | parent [-]

I'm frustrated by the number of times I encounter people assuming that the current model behavior is inevitable. There's been hundreds of billions of dollars spent on training LLMs to do specific things. What exactly they've been trained on matters; they could have been trained to do something else.

Interacting with a base model versus an instruction tuned model will quickly show you the difference between the innate language faculties and the post-trained behavior.

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Workaccount2 3 days ago | parent [-]

Some of the Anthropic guys have said that the core thing holding the models back is training, and they're confident the gains will keep coming as they figure out how to onboard more and more training data. So yeah, Claude might suck at reading and writing plumbing diagrams, but they claim the barrier is simply a function of training, not any kind of architectural limitation.

	▲	ACCount37 3 days ago \| parent [-]
		I agree with the general idea, but "sucks at reading plumbing diagrams" is the one specific example where Claude is actually choked by its unfortunate architecture. The "naive" vision implementation for LLMs is: break the input image down into N tokens and cram those tokens into the context window. The "break the input image down" part is completely unaware of the LLM's context, and doesn't know what data would be useful to the LLM at all. Often, the vision frontend just tries to convey the general "vibes" of the image to the LLM backend, and hopes that the LLM can pick out something useful from that. Which is "good enough" for a lot of tasks, but not all of them, not at all.