CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers
<|reference_start|>CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers: 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.<|reference_end|>
@article{trofimova2024coderefine:,
    title={CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations
  of Research Papers},
    author={Ekaterina Trofimova, Emil Sataev, Abhijit Singh Jowhari},
    journal={arXiv preprint arXiv:2408.13366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13366},
    primaryClass={cs.CL cs.AI cs.LG}
}