Opinion: All LLMs have something like Wernicke’s aphasia and we should use that to define their use cases

Bio major here, so that kind of stuff is my language. Wernicke’s aphasia is a phenomenon where people have trouble with language comprehension, but not production. People can make speech that’s perfectly grammatically correct and fluent (sometimes overly fluent) but nonsensical and utterly without meaning. They make new words, use the wrong words, etcetera. I think this is a really good example for how LLMs work.

Essentially, I posit that LLMs are the equivalent of finding a patient with this type of aphasia – a disconnect between the language circuits and the rest of the brain – and, instead of trying to reconnect them, making a whole building full of more Wernicke’s area, massive quantities of brain tissue that don’t do the intended job but can be sort of wrangled into kind of doing the job by their emergent properties. The sole task is to make sure language comes out nicely. When taken to its extreme, it indirectly ‘learns’ about the world that language defines, but it still doesn’t actually handle it properly, it’s pure pattern-matching.

I feel like this might be a better analogy than the stochastic parrot, but I wanted to pose it somewhere where people could tell me if I’m just an idiot/suffering from LLM-induced psychosis. I think LLMs should really be relegated to linguistic work. Wire an LLM into an AGI consisting of a bunch of other models (using neuralese, of course) and the LLM itself can be tiny. I think these gigantic models and all this stuff about scaling is the completely wrong path, and that it’s likely we’ll be able to build better AI for WAY cheaper by aggregating various small models that each do small jobs. An isolated chunk of Wernicke’s area is pretty useless, and so are the smallest LLMs, we’ve just been making them bigger and bigger without grounding them.

Just wanted to post to ask what people think.