Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K

Long-range context modeling for software vulnerability detection using an XLNet-based approach.

Yinhu Zhao1, Guanjun Lin2,3, Zhenxuan Liao4,5

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

Scientific Reports
|January 16, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adaptive Differentiated Parrot Optimization: A Multi-Strategy Enhanced Algorithm for Global Optimization with Wind Power Forecasting Applications.

Biomimetics (Basel, Switzerland)·2025
Same author

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide.

Journal of environmental management·2024
Same author

Identification of surface urban heat versus cool islands for arid cities depends on the choice of urban and rural definitions.

The Science of the total environment·2024
Same author

Surface plasmon enhanced photochemical etching of p-type GaP: a direct demonstration of wavelength selectivity.

Physical chemistry chemical physics : PCCP·2014
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles
This summary is machine-generated.

XLNetVD enhances software vulnerability detection by using XLNet to capture long code dependencies, outperforming existing models with a 68% F1-score. This framework offers a state-of-the-art solution for identifying vulnerabilities in code.

Area of Science:

  • Cybersecurity
  • Software Engineering
  • Artificial Intelligence

Background:

  • Software vulnerability detection is crucial for cybersecurity.
  • Language Model (LM)-based approaches show promise but struggle with long-range code dependencies.
  • Transformer architectures have limitations in capturing extensive code context.

Purpose of the Study:

  • Introduce XLNetVD, an XLNet-based framework for function-level vulnerability detection.
  • Address the limitations of existing models in capturing long-range code dependencies.
  • Evaluate the effectiveness of XLNet for vulnerability detection.

Main Methods:

  • Leveraged a bidirectional Transformer-XL model for extended context modeling.
  • Benchmarked XLNet against six contextual and three non-contextual embedding models.
Keywords:
Contextual embedding modelsEnd-to-end detect frameworkLoRA model compressionVulnerability detection

Related Experiment Videos

Last Updated: Jan 18, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

1.0K
  • Integrated XLNet into an end-to-end framework, XLNetVD, and applied Low-Rank Adaptation (LoRA) fine-tuning.

    • Main Results:

    • XLNet achieved the best F1-score of 68%, surpassing CodeBERT and GPT-2.
    • XLNet-LoRA demonstrated the best performance-efficiency trade-off among LoRA-enhanced LMs.
    • XLNetVD showed competitive performance on both imbalanced real-world and balanced SARD datasets.

    Conclusions:

    • XLNetVD establishes itself as a state-of-the-art solution for software vulnerability detection.
    • The framework effectively captures essential code dependencies for identifying subtle vulnerabilities.
    • XLNet-based approaches offer significant improvements over existing LM-based methods.