It depends what you mean by combine. They work in fundamentally different ways. It's a bit like combining an internal combustion engine with a ramjet.

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.

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.

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.