Noga Amit

A Theory for Worst-Case vs. Average-Case Guarantees for LLMs

How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured *on average* over a distribution of inputs, giving no guarantee for any fixed input. This paper proposes a theoretically-founded solution to this problem: to train *Self-Provin…