LLMs are useful for things like predicting/generating text, and summarizing text. They are not useful if you want to do other NLP tasks that include things like:
1. Identifying (and highlighting/extracting) the language that spans of text are in within a different language (e.g. a French phrase in English).
2. Text search and highlighting, where you need to do things like performing word stemming or lemmatization on the search input, find matching documents, and highlight spans.
Also, LLMs have a different tokenization model, so you have tokens like "don"/"'t!" -- this makes them difficult to work with in downstream NLP applications where having words, numbers, punctuation, and symbols as specific tokens are more useful.
NLP is not a solved task, as things like part of speech classification (identifying nouns, adjectives, etc.) are not 100% accurate, and tend to have a lower sentence accuracy compared to word accuracy.
1. Identifying (and highlighting/extracting) the language that spans of text are in within a different language (e.g. a French phrase in English).
2. Text search and highlighting, where you need to do things like performing word stemming or lemmatization on the search input, find matching documents, and highlight spans.
Also, LLMs have a different tokenization model, so you have tokens like "don"/"'t!" -- this makes them difficult to work with in downstream NLP applications where having words, numbers, punctuation, and symbols as specific tokens are more useful.
NLP is not a solved task, as things like part of speech classification (identifying nouns, adjectives, etc.) are not 100% accurate, and tend to have a lower sentence accuracy compared to word accuracy.