Multimodal Fine-Tuned Small Language Models vs. Large Multimodal LLMs in CWE Detection Accuracy

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: How do small language models (SLMs) fine-tuned with multimodal context compare to larger multimodal LLMs in terms of CWE detection accuracy and alignment metrics on the extended Big-Vul dataset. In this paper, we present a challenging code reasoning task: vulnerability detection. Large Language Models (LLMs) have shown promising results in natural-language and math reasoning, but state-of-the-art (SOTA) models reported only 54.5\% Balanced Accuracy in our vulnerability. 6 claims were extracted from source literature; 6 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 Question

How do small language models (SLMs) fine-tuned with multimodal context compare to larger multimodal LLMs in terms of CWE detection accuracy and alignment metrics on the extended Big-Vul dataset?

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