CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers

CodeRefine is a new AI framework that automatically converts research paper methodologies into functional code using Large Language Models. The system creates knowledge graphs from papers and uses retrieval-augmented generation to produce more accurate code implementations than traditional zero-shot prompting methods.

• →CodeRefine uses a multi-step pipeline to extract key information from research papers and generate corresponding code implementations.
• →The framework creates knowledge graphs using predefined ontologies to structure paper content before code generation.
• →A retrospective retrieval-augmented generation approach enhances code quality beyond standard LLM prompting.
• →The system aims to bridge the gap between theoretical research and practical implementation in software development.
• →Evaluations show improved code implementation accuracy across diverse scientific papers compared to zero-shot methods.