That said, it's likely that you'll get good zero-shot performance, so the model building phase could benefit from fine-tuning the prompt given the dataset - instead of training the underlying model itself.
That said, it's likely that you'll get good zero-shot performance, so the model building phase could benefit from fine-tuning the prompt given the dataset - instead of training the underlying model itself.
For example, take the classic "house price prediction" problem. We don't use an LLM to make the predictions, we use LLMs to model the problem and write code that trains an ML models to predict house prices. This would most likely end up being an xgboost regressor or something like that.
As to your point about evals, great question! We've done some testing but haven't yet carried out a systematic eval. We intend to run this on OpenAI's MLE-Bench to quantify how well it actually does as creating models.
Hope I didn't misunderstand your comment!