CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers
This addresses the problem of bridging theoretical research and practical implementation for researchers and practitioners, though it appears incremental as an enhancement over existing LLM-based approaches.
The paper tackles the challenge of automatically transforming research paper methodologies into functional code using Large Language Models (LLMs), presenting CodeRefine, a multi-step pipeline that extracts and structures paper content to generate and enhance code. Evaluations show it improves code implementation from papers, offering a more accurate alternative to zero-shot prompting.
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. Evaluations on diverse scientific papers demonstrate CodeRefine's ability to improve code implementation from the paper, potentially accelerating the adoption of cutting-edge algorithms in real-world applications.