[AmIDev]

LLMs are extremely useful for ML classification.

1. If you have a large amount of unlabeled data, labeling it using LLMs(even a costly XXXB parameter model) can be significantly cheaper than using humans.

2. Text classification works far better with LLMs compared to usual techniques like random forests.

3. We are exploring using LLMs on structured data(tables) for tasks like clustering, where it is difficult to tune other unsupervised approaches. Similarly we are exploring using LLM embeddings for similarity on unsupervised datasets.

4. LLMs could explain their decisions to users(e.g. why was your comment removed?) although work needs to be done on verifying it's correctness.

I wouldn't be surprised if LLM takes the spot of Random forest as the default go to for supervised ML.

[Tostino]

Yup, and you can fine-tune an LLM to be a specialized classifier with relatively little data compared to building a classifier from scratch. I think that's the biggest benefit to LLMs, the data required to show it a behavior is pretty minuscule in the scheme of things.