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

Understand

This paper presents CodeRefine, a novel framework for automatically transforming research paper methodologies into functional code using Large Language Models (LLMs).

• Our multi-step approach first extracts and summarizes key text chunks from papers, analyzes their code relevance, and creates a knowledge graph using a predefined ontology.
• Code is then generated from this structured representation and enhanced through a proposed retrospective retrieval-augmented generation approach.
• CodeRefine addresses the challenge of bridging theoretical research and practical implementation, offering a more accurate alternative to LLM zero-shot prompting.