Scaling Laws of Vulnerability Classification Accuracy in Llama3, Codestral, and DeepSeek R1

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the vulnerability classification accuracy of Llama3, Codestral, and Deepseek R1 scale with increasing model size when evaluated on technical domain benchmarks like Big-Vul compared to. The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. 15 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the vulnerability classification accuracy of Llama3, Codestral, and Deepseek R1 scale with increasing model size when evaluated on technical domain benchmarks like Big-Vul compared to general conversational datasets?

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