Are you planning on submitting this model to be evaluated against the Spider holdout set?

Also, wondering if anyone has found research on the inverse of this approach to the problem, i.e., instead of training the model to understand the data, you improve the data to be more understandable to a model? This seems more promising when you are looking at enterprise use cases without much training data. Spider seems like quite a simple dataset compared to the ones I encounter on the job, and LLMs struggle even with those.