Robustness of Retrieval-Augmented Generation in Llama3-70B and Gemini 1.5 Pro on CodeXGLUE Security
Abstract
This report synthesises findings from 2 peer-reviewed papers addressing the following research question: How does the robustness of retrieval-augmented generation compare between Llama3-70B and Gemini 1.5 Pro on the CodeXGLUE security subset when evaluated using the EM (Exact Match) metric under. 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 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the robustness of retrieval-augmented generation compare between Llama3-70B and Gemini 1.5 Pro on the CodeXGLUE security subset when evaluated using the EM (Exact Match) metric under adversarial perturbations? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.
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