Instruction-Tuned Codestral-7B and Llama3-70B Cross-Domain Generalization in Security Vulnerability Detection

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does the cross-domain generalization accuracy of fine-tuned Codestral-7B compare to Llama3-70B on unseen programming languages beyond Python for security vulnerability classification. Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the cross-domain generalization accuracy of fine-tuned Codestral-7B compare to Llama3-70B on unseen programming languages beyond Python for security vulnerability classification

Autonomous literature synthesis. Automated review score: 7.7/10. Full text and citation available at Assignee Research.