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English(EN) Efficient Code Embeddings from Code Generation Models

新方法利用大型语言模型进行高级源代码嵌入 · 跟踪2个来源

两篇新研究论文介绍了使用大型语言模型进行源代码嵌入的新方法。第一种方法 LSem2Vec 结合了大型语言模型和句子嵌入模型,无需任务特定的微调即可提取代码语义,在各种编程语言上表现优异。第二篇论文介绍了 jina-code-embeddings,这是一套更小、更高效的模型,利用在文本和代码上训练的自回归骨干网络,在自然语言代码检索和语义相似性识别等任务上取得了最先进的成果。 AI

影响 这些进展可以改进软件工程工作流程中的代码分析、检索和理解。

排序理由 两篇在 arXiv 上发表的学术论文,介绍了源代码嵌入的新方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新方法利用大型语言模型进行高级源代码嵌入 · 跟踪2个来源

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两篇在 arXiv 上发表的学术论文,介绍了源代码嵌入的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zixiang Xian, Chenhui Cui, Rubing Huang, Chunrong Fang, Zhenyu Chen ·

    LSem2Vec:一种简单而有效的源代码嵌入两阶段方法

    arXiv:2409.14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a crucial role in tasks such as source code clone detection and sou…

  2. arXiv cs.AI TIER_1 English(EN) · Daria Kryvosheieva, Saba Sturua, Michael G\"unther, Han Xiao ·

    高效代码生成模型的代码嵌入

    arXiv:2508.21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming l…