Cross-Domain vs. In-Domain Finetuning Effects on DeepSeek-V3 GPQA Diamond Accuracy

This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How does cross-domain finetuning affect DeepSeek-V3's accuracy on GPQA Diamond compared to in-domain finetuning. As Large Language Models (LLMs) become increasingly integrated into secure software development workflows, a critical question remains unanswered: can these models not only detect insecure code but also reliably classify vulnerabilities according to standardized taxonomies? In. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does cross-domain finetuning affect DeepSeek-V3's accuracy on GPQA Diamond compared to in-domain finetuning?

Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.