一篇新发表在 arXiv 上的研究论文探讨了提示语言对不同大语言模型（LLMs）代码生成质量的影响。研究人员发现，用于提示 GPT-4o mini、DeepSeek 和 Claude 等模型的语言会显著影响生成代码的功能正确性和结构质量。该研究使用了包含 460 个 Python 和 Java 编码任务的基准，并将提示翻译成中文、印地语、西班牙语和意大利语，结果显示英文提示并不总是能产生最佳结果，并且生成的代码有时会混合语言。 AI
影响 这项研究强调了进行多语言提示工程的必要性，以优化不同大语言模型和编程语言的代码生成质量。
排序理由 该集群包含一篇详细介绍大语言模型代码生成基准和研究的论文。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.14816v1 Announce Type: cross Abstract: Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text genera…
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and pr…
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general text generation, its impact on code generation quality and pr…