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

• URL: http://arxiv.org/abs/2408.13366v1
• Date: Fri, 23 Aug 2024 20:51:04 GMT
• Title: CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers
• Authors: Ekaterina Trofimova, Emil Sataev, Abhijit Singh Jowhari,
• Abstract summary: CodeRefine is a framework for transforming research paper methodologies into functional code using Large Language Models. Our multi-step approach first extracts and summarizes key text chunks from papers, analyzes their code relevance, and creates a knowledge graph. Code is then generated from this structured representation and enhanced through a proposed retrospective retrieval-augmented generation approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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