Zero-Shot Visual Language Models vs. Fine-Tuned Code-Specific Multimodal Models on Unseen CWE Benchmarks

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How do zero-shot Visual Language Models like Flamingo compare to fine-tuned code-specific multimodal models in terms of accuracy on unseen CWE categories in benchmarks like CWESec and SARD. Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. 17 claims were extracted from source literature; 16 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 Question

How do zero-shot Visual Language Models like Flamingo compare to fine-tuned code-specific multimodal models in terms of accuracy on unseen CWE categories in benchmarks like CWESec and SARD?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance