I’ve had similar thoughts, wondering why LLMs aren’t just a component of a larger system, perhaps used to generate “thoughts” that are then tested by some other subsystems, which in turn may re-fire the LLMs to generate more.
I guess I’m imagining that testing the LLM output might be a problem that some other technique might be suited for, and smells like what we need to mitigate LLM weaknesses.
Re:Understanding Large Language Models
by jonathanstrange on Tuesday April 18, 2023 @04:34AM
(#35611431)
What I had in mind was to make the output of a natural language parser into symbolic semantic representations (the folks at CYC have such parsers) an additional part of the training data set of the LLM.
You might be able to compose systems that use LLMs for some sub-components and symbolic AI for other functions. So for example if ChatGPT gets asked a question that LLMs are poor at but symbolic systems are good at, it could switch to using a symbolic system, but that's not really combining the actual technologies.