Fine-Tuning mT5 on Code-Specific vs. General Datasets for Cross-Lingual Vulnerability Detection

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of fine-tuning mT5 on code-specific datasets versus general textual datasets on its cross-lingual vulnerability detection accuracy, and does this vary with the typological distance. Abstract In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. 12 claims were extracted from source literature; 12 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the impact of fine-tuning mT5 on code-specific datasets versus general textual datasets on its cross-lingual vulnerability detection accuracy, and does this vary with the typological distance between languages?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.