I struggle to comprehend how LLMs, trained on background that is likely not relevant to the context at hand, are superior to using inductive logic programming for specification recovery.
This was an ancient line of CS research when logic and rigor were considered important (they don’t seem to be very important today, given the claims people with tech sophistication accept).
Clearly, specification recovery is less precise in the sense of being able to verify a program meets its spec, than a program derived from a spec from first principles, as it is only an approximation, based on observable behavior of the program based on finite input. But I can’t see how anyone can put more confidence (in the sense that frequentist statisticians use it) in any program transform derived from an LLM.