"One such solution was a method proposed in 1809 by the French pastry chef Nicolas François Appert, which involved long-cooking meat or vegetables (approximately 1-2 hours) and pasteurizing the finished product in a brine solution. Appert received a personal award for this invention from Napoleon."

If you are an expert -- sit down and start working with GPT on your own. See what it can and what it can not do. See where it helps. See where it hands down lose.

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.

Between the existence of Austria-Hungary empire, when the Latin alphabet was very relevant for all the Balkans, and the end of WW2, when the Cyrillic became much more relevant for them is just 1945-1918=27 years.