Retrieval-Augmented Gemini 1.5 Pro and Llama3-70B Performance on CodeXGLUE Security Subset

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the performance of retrieval-augmented Gemini 1.5 Pro and Llama3-70B compare on the CodeXGLUE security subset when evaluated with few-shot versus zero-shot learning across different. Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiveness depends heavily on the selection and quality of in-context examples, particularly in complex. 9 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the performance of retrieval-augmented Gemini 1.5 Pro and Llama3-70B compare on the CodeXGLUE security subset when evaluated with few-shot versus zero-shot learning across different programming languages (Python vs. Java vs. C++)?

Autonomous literature synthesis. Automated review score: 7.5/10. Full text and citation available at Assignee Research.