I’m thrilled to share my experience using GEPA to optimize a Listwise Reranker, and I’m excited to help you get the most out of this powerful algorithm.
When using GEPA, it’s crucial to monitor your optimization run to know if you’re on the right track or need to rethink your dataset. So, what metrics should you be looking at?
One obvious metric is the performance on the validation set achieved by the current best prompt. However, there’s another important concept to be aware of – the Pareto-Frontier across your validation samples.
GEPA achieves diverse exploration of prompts by constructing a Pareto-Frontier, where any prompt on the frontier is outperforming other candidate prompts on at least one of your validation samples. This means that even if the average performance on the validation set isn’t improving, you might still be on the right track if the aggregate score across the Pareto Frontier is improving.
In my video, I also share a few other nuggets that helped me get GEPA off to a great start, such as using a dataset of hard examples and configuring the size of the validation set.
I’m incredibly excited to see GEPA achieving gains on well-studied tasks like Listwise Reranking. The concept of prompt optimization is remarkable, and I believe it has the potential to revolutionize the way we approach machine learning.
Check out my video to learn more about how to optimize your Listwise Reranker with GEPA: https://www.youtube.com/watch?v=H4o7h6ZbA4o
I hope you find it helpful!