大模型如何让普通人用自然语言生成代码
Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications
- 分析大模型在代码生成中的局限与挑战
- 总结微调技术提升模型生成能力的方法
- 适合对AI编程工具感兴趣的开发者和研究者
大语言模型在多个领域展现出卓越能力。本综述聚焦于大模型如何使用户（无论技术背景如何）能够通过自然语言自动生成可执行代码。首先，探讨大模型在自动化代码生成中的局限与挑战；随后，回顾为提升模型在代码生成任务中性能与适应性的各类微调技术；接着，梳理现有评估指标与基准测试，以衡量不同微调方法下的模型表现；最后，探讨代码生成应用（如CodeLlama、GitHub Copilot、ToolGen）的实际角色与功能。本综述全面概述了大模型在代码生成中的进展，帮助跨领域研究者理解当前最先进技术，并提供有效利用大模型进行代码生成的潜力。
原文摘要 · Abstract (English)
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.
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