Cross-Domain Fine-Tuning Effects on Chain-of-Thought Quality in Code Generation

This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does cross-domain fine-tuning (e.g., pre-training on Python vs. Java) impact the CoT step quality for code generation on BigCodeBench, evaluated using functional correctness scores. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 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 does cross-domain fine-tuning (e.g., pre-training on Python vs. Java) impact the CoT step quality for code generation on BigCodeBench, evaluated using functional correctness scores?

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