This work presents Past as a Guide (PaG), a simple approach for Large
Language Models (LLMs) to improve the coding capabilities by integrating the
past history with interactive and iterative code refinements. To be specific,
inspired by human cognitive processes, the proposed method enables LLMs to
utilize previous programming and debugging experiences to enhance the Python
code completion tasks. The framework facilitates LLMs to iteratively refine the
Python code based on previous execution and debugging results and optimize
learning and reasoning capabilities. The proposed methodology achieved a 92\%
pass@1 on HumanEval, demonstrating the potential to advance the field by
leveraging retrospection from past experiences and interactive and iterative
refinement processes without external correctness indicators.

这项工作提出了过去作为指南（PaG），这是一种用于大型语言模型（LLMs）的简单方法，通过将过去的历史与交互和迭代的代码改进相结合，提高编码能力。具体而言，受人类认知过程的启发，该方法使 LLMs 能够利用以往的编程和调试经验，提升 Python 代码自动完成任务的能力。该框架使 LLMs 能够基于先前执行和调试结果进行迭代式的代码优化，提高学习和推理能力。借助过去经验的回顾和交互迭代的改进过程，该方法在 HumanEval 上取得了 92％的一次通过率，展示了在没有外部正确性指标的情况下利用过去经验推进领域发展的潜力。