Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code. We begin with understanding LLMs' limitations and challenges in automated code generation. Subsequently, we review various fine-tuning techniques designed to enhance both the performance and adaptability of LLMs in code generation tasks. We then review the existing metrics and benchmarks for evaluations to assess model performance based on fine-tuning techniques. Finally, we explore the applications of LLMs (e.g. CodeLlama, GitHub Copilot, ToolGen) in code generation tasks to illustrate their roles and functionalities. This survey provides a comprehensive overview of LLMs for code generation, helps researchers in diverse fields better understand the current state-of-the-art technologies, and offers the potential of effectively leveraging LLMs for code generation tasks.

翻译：大语言模型（LLMs）已在众多领域展现出卓越能力。本综述聚焦于LLMs如何赋能用户——无论其技术背景如何——使用人类语言自动生成可执行代码。我们首先理解LLMs在自动化代码生成中的局限性与挑战。随后，我们回顾了旨在提升LLMs在代码生成任务中性能与适应性的多种微调技术。接着，我们梳理了现有用于评估的指标与基准，以基于微调技术评估模型性能。最后，我们探讨了LLMs（如CodeLlama、GitHub Copilot、ToolGen）在代码生成任务中的应用，以阐明其角色与功能。本综述为代码生成的LLMs研究提供了全面概览，有助于不同领域的研究者更好地理解当前前沿技术，并为有效利用LLMs进行代码生成任务提供了潜在方向。