Semantic Similarity Metrics and False Positive Rates in DeepSeek-V3 Vulnerability Detection

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: To what extent does the semantic similarity metric used for retrieving few-shot examples impact the false positive rate of DeepSeek-V3 on the Big-Vul benchmark compared to random example selection. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. 10 claims were extracted from source literature; 10 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 the semantic similarity metric used for retrieving few-shot examples impact the false positive rate of DeepSeek-V3 on the Big-Vul benchmark compared to random example selection?

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