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.
@article{arxiv.2408.13366,
title = {CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers},
author = {Ekaterina Trofimova and Emil Sataev and Abhijit Singh Jowhari},
journal= {arXiv preprint arXiv:2408.13366},
year = {2026}
}
Comments
The results mentioned in the paper are non-reproducible. We have rechecked the metrics, and they do not match with the ones that have been provided in the paper. Therefore, we accept that this article is neither suitable nor up to the mark for the scientific community and must be with-drawn. We fully understand the consequences, and would like to wishfully retract this article