Large language models (LLMs) have already revolutionized code generation,
after being pretrained on publicly available code data. However, while various
methods have been proposed to augment LLMs with retrieved knowledge and enhance
the quality of code generation, the performance of these retrieval-based
methods is limited by the strength of the retrievers used. In addition, while
LLMs show great emergent ability, they still struggle to produce the correct
code in one turn. To address these challenges, we propose a novel two-step
pipeline, called \autoknow, that leverages LLMs as both knowledge providers and
self-reflective programmers. Unlike retrieval-based methods, \autoknow~obtains
the knowledge from input prompts and generates intermediate code based on the
generated knowledge. After that, \autoknow~asks LLM to act as an expert
programmer to perform debugging for the generated code. This is achieved by
receiving the error message from the interpreter, without requiring special
test cases for correctness verification. We evaluate \autoknow~on three code
generation datasets, including DS-1000 for data science code, HumanEval for
software engineering code, and TransCoder for C++-to-Python translation. Our
empirical experiments show that \autoknow~outperforms strong baselines by a
significant margin on all datasets. We also conduct exhaustive analytical
experiments to validate the effectiveness of the two stages of \autoknow, and
find that both are superior to other prompting-based methods. Further
scalability analysis demonstrates that \autoknow~can be adapted to other more
advanced models, such as GPT-4, and bring consistent efficacy improvement.

提出了一种名为 AutoKnow 的新型两步流程，将 LLMs 作为知识提供者和自我反思程序员，通过从输入提示中获得知识并根据生成的知识生成中间代码，并存在解释器中接收错误消息，从而使 LLMs 成为专业编程人员，从而成功完成编程，有效提高了二者的表现。