[roadside_picnic]

> like entity recognition

As someone who has done traditional NLP work as at least part of my job for the last 15 years, LLMs do ofter a vastly superior NER solution over any previous NLP options.

I agree with your overall statement, that frequently people rush to grab an LLM when superior options already exist (classification is a big example, especially when the power of embeddings can be leveraged), but NER is absolutely a case where LLMs are the superior option (unless you have latency/cost requirements to force you to choose and inferior quality as the trade off, but your default should be an LLM today).

[Centigonal]

Also agree. I spent so much time messing with fuzzy matching libraries and NERs for various entity resolution tasks, collecting and cleaning lists of various entity types, and so forth. IMO you really need a model with the encoded world knowledge of an LLM to reliably and flexibly make determinations like that "WMT" and "wally world" are referring to the same corporate entity.

[mark_l_watson]

I agree! I used 'symbolic AI' for NLP starting in the early 1980s. Everything back then was so brittle, and very labor intensive.