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Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

用基于XLNet的方法进行软件漏洞检测的远程上下文建模,使用基于XLNet的方法.

Yinhu Zhao¹, Guanjun Lin^2,3, Zhenxuan Liao^4,5

¹School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, 350118, China.

Scientific reports

|January 16, 2026

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此摘要是机器生成的。

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XLNetVD通过使用XLNet来捕获长代码依赖性来增强软件漏洞检测,以68%的F1得分优于现有模型. 这个框架提供了一个最先进的解决方案,用于识别代码中的漏洞.

科学领域:

网络安全网络安全
软件工程软件工程软件工程
人工智能的人工智能

背景情况:

软件漏洞检测对于网络安全至关重要.
基于语言模型 (LM) 的方法显示出希望,但与远程代码依赖性作斗争.
变压器架构在捕获广泛的代码上下文方面存在局限性.

研究的目的:

介绍XLNetVD,这是一个基于XLNet的框架,用于功能级别的漏洞检测.
解决现有模型在捕获远程代码依赖性方面的局限性.
评估XLNet在漏洞检测方面的有效性.

主要方法:

利用双向变压器-XL模型进行扩展的上下文建模.
与六个上下文和三个非上下文嵌入模型对比的XLNet.
将XLNet集成到一个端到端的框架中,XLNetVD,并应用低级调整 (LoRA) 微调.

主要成果:

XLNet获得了最高的F1得分68%,超过了CodeBERT和GPT-2.
在LoRA增强的LM中,XLNet-LoRA显示出最佳的性能效率权衡.
在不平衡的真实世界和平衡的SARD数据集上,XLNetVD表现出了竞争力.

关键词:

语境嵌入模型语境嵌入模型一个端到端检测框架.在LoRA模型的压缩模式下,漏洞检测发现漏洞的检测

相关实验视频

Last Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

结论:

XLNetVD 确立了自己作为软件漏洞检测的最先进的解决方案.
该框架有效地捕捉了必要的代码依赖性,用于识别微妙的漏洞.
基于XLNet的方法比现有的基于LM的方法提供了显著的改进.