  In 2023, we are using the latest models of GPT-4 to advance program
synthesis. The large language models have significantly improved the
state-of-the-art for this purpose. To make these advancements more accessible,
we have created a repository that connects these models to Huamn Eval. This
dataset was initally developed to be used with a language model called CODEGEN
on natural and programming language data. The utility of these trained models
is showcased by demonstrating their competitive performance in zero-shot Python
code generation on HumanEval tasks compared to previous state-of-the-art
solutions. Additionally, this gives way to developing more multi-step paradigm
synthesis. This benchmark features 160 diverse problem sets factorized into
multistep prompts that our analysis shows significantly improves program
synthesis over single-turn inputs. All code is open source at
https://github.com/daniel442li/gpt-human-eval .
