[horsh1]

Unless we intend to surrender everything about human symbolic manipulations (all math, all proving, all computations, all programming) to llm in the nearest future, we still need some formal representations for engineering.

The major part of tradidional NLP was about formal representations. We are still to see the efficient mining techniques to extract the formal representations and analyses back from LLM.

How would we solve the traditional NLP problems, such as, for example, formalization of law corpus of a given country with LLM?

As an approximation we can look at non-natural language processing, e.g. compiler technologies. How do we write an optimizing compiler on LLM technologies? How do we ensure stability, correctness and price?

In a sence, the traditional NLP field has just doubled, not died. In addition to humans as language capable entities, who can not really explain how they use the language, we now also have LLM as another kind of language capable entities. Who in fact also can not explain anything. The only benefit is that it is cheaper to ask LLM the same question a million of times that a human.