Long-Context Gemini 1.5 Pro vs Retrieval-Augmented Llama3-70B in Multi-File Code Vulnerability Classification

Abstract

Abstract: This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the performance of Gemini 1.5 Pro with an 8M context window compare to Llama3-70B with retrieval augmentation in classifying vulnerabilities on the CodeXGLUE security subset when the input. Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks, known as Code LLMs, particularly in code generation that generates source code with LLM from natural language descriptions. This burgeoning field has captured significant. 11 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.6/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the performance of Gemini 1.5 Pro with an 8M context window compare to Llama3-70B with retrieval augmentation in classifying vulnerabilities on the CodeXGLUE security subset when the input spans 10+ files, and what is the impact on precision-recall tradeoff?

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