Context Window Scaling and Pass@1 Accuracy in Code Llama for Cross-Library API Generation

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does increasing context window size affect pass@1 accuracy on BigCodeBench for Code Llama variants during cross-library API generation tasks. 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. 6 claims were extracted from source literature; 6 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 increasing context window size affect pass@1 accuracy on BigCodeBench for Code Llama variants during cross-library API generation tasks?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.