Most definitely! LLMs are amazing tools for generating synthetic datasets that can be used alongside traditional NLP to train things like decision trees with libraries like cat/xgboost.

I have a search background so learning to rank is always top of mind for me, but there other places like sentiment analysis, intent detection, and topic classification where it's great too.

Some low hanging fruit: SpaCy makes an amazing chunking tool for preprocessing text for LLMs.

I used to work a lot with those pipelines, I think the truth is that LLMs (and LLM embeddings) have surpassed pretty much all traditional NLP. I guess if speed is more important than accuracy? but even then, like with small embedded LLMs they still outperform "traditional NLP" on pretty much every task probably. So it doesn't make a lot of sense to not use it nowadays.