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English(EN) To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

新的PriCoder方法教大型语言模型使用私有库进行代码生成

研究人员开发了PriCoder,一种增强大型语言模型(LLMs)生成利用私有库代码的能力的新方法。当前方法即使有API文档的访问权限也表现不佳,因此催生了PriCoder,它通过合成数据来教会LLMs如何有效地调用这些私有API。该方法将数据合成建模为一个图构建问题,采用渐进式图演化来保证多样性,并通过多维图剪枝来保证质量。实验表明,PriCoder在不影响通用编码能力的情况下,将私有库代码生成性能显著提升了20%以上。 AI

影响 增强了LLMs在专业代码生成方面的能力,有望改进开发工具和工作流程。

排序理由 该集群包含一篇详细介绍LLM代码生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的PriCoder方法教大型语言模型使用私有库进行代码生成

本文如何被排名

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17 / 100
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Tool
该集群包含一篇详细介绍LLM代码生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
paper, model release
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High
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完整方法见我们的编辑标准

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yitong Zhang, Chengze Li, Ruize Chen, Guowei Yang, Xiaoran Jia, Yijie Ren, Jia Li ·

    看见不等于掌握:教大型语言模型使用私有库进行代码生成

    arXiv:2603.15159v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate code using APIs from private libraries. Exist…