37% gain relative to what? What percent of generated functions were incorrect?
compared to vanilla LLM decoding.
Several people mentioned the generation - compilation - fixing loop. Just want to remind you that our approach works for not only the generation step but also the fixing step. This is because fixing is essentially asking LLMs to generate a new version of the code. The paper actually has a "repair" experiment to demonstrate this and our approach achieves significant gain in this experiment, i.e., 37% relative improvement in functional correctness.