Cross-Language Fine-Tuning Effects on Zero-Shot Vulnerability Detection in SLMs

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: To what extent does cross-language fine-tuning affect the zero-shot vulnerability detection accuracy of SLMs on the Devign dataset. This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent does cross-language fine-tuning affect the zero-shot vulnerability detection accuracy of SLMs on the Devign dataset?

Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.