Cross-Domain Robustness of Fine-Tuned Codestral-7B and Llama3-70B in Low-Resource Code Generation

This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How robust are fine-tuned Codestral-7B and Llama3-70B models when evaluated on cross-domain code generation tasks in low-resource languages. Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current methods either rely on an encoder-only (or. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How robust are fine-tuned Codestral-7B and Llama3-70B models when evaluated on cross-domain code generation tasks in low-resource languages

Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.