Comment by oliwary

This article seems to be paywalled unfortunately. While LLMs are very useful when the tasks are complex and/or there is not a lot of training data, I still think traditional NLP pipelines have a very important role to play, including when:

- Depending on the complexity of the task and the required results, SVMs or BERT can be enough in many cases and take much lower resources, especially if there is a lot of training data available. Training these models with LLM outputs could also be an interesting approach to achieve this.

- When resources are constrained or latency is important.

- In some cases, there may be labeled data in certain classes that have no semantic connection between them, e.g. explaining the class to LLMs could be tricky.