Small Language Models vs. Domain-Adapted Models in Multimodal CWE Detection

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the accuracy difference between SLMs and domain-adapted models on a multimodal benchmark (e.g., combining code and natural language descriptions) for CWE detection, and how does this vary. Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. 11 claims were extracted from source literature; 9 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 Question

What is the accuracy difference between SLMs and domain-adapted models on a multimodal benchmark (e.g., combining code and natural language descriptions) for CWE detection, and how does this vary with adversarial perturbations?

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