  Can LLMs continually improve their previous outputs for better results? An
affirmative answer would require LLMs to be better at discriminating among
previously-generated alternatives, than generating initial responses. We
explore the validity of this hypothesis in practice. We first introduce a
unified framework that allows us to compare the generative and discriminative
capability of any model on any task. Then, in our resulting experimental
analysis of several LLMs, we do not observe the performance of those models on
discrimination to be reliably better than generation. We hope these findings
inform the growing literature on self-improvement AI systems.
