[2408.13366] CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers

Computer Science > Computation and Language arXiv:2408.13366 (cs) This paper has been withdrawn by Abhijit Jowhari [Submitted on 23 Aug 2024 (v1), last revised 26 Mar 2026 (this version, v2)] Title:CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers Authors:Ekaterina Trofimova, Emil Sataev, Abhijit Singh Jowhari View a PDF of the paper titled CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers, by Ekaterina Trofimova and 2 other authors No PDF available, click to view other formats 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. Comments: Subjects: Computation and Language (cs.CL); Artifi...