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.

Related papers

• RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation [13.75248879205993]
  We propose Adaptive Critique Refinement (ACR), which enables the model to refine itself by self-generated code and external critique. ACR includes a composite scoring system with LLM-as-a-Judge to evaluate the quality of code responses. We develop the RefineCoder series by iteratively applying ACR, achieving continuous performance improvement on multiple code generation benchmarks.
  arXiv  Detail & Related papers  (2025-02-13T11:17:53Z)
• Enhancing Code Consistency in AI Research with Large Language Models and Retrieval-Augmented Generation [0.0]
  This paper presents a novel system designed to verify code implementations against the algorithms and methodologies outlined in corresponding research papers. Our system employs Retrieval-Augmented Generation to extract relevant details from both the research papers and code bases, followed by a structured comparison using Large Language Models.
  arXiv  Detail & Related papers  (2025-02-02T00:35:42Z)
• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval [103.116634967815]
  We introduce CodeXEmbed, a family of large-scale code embedding models ranging from 400M to 7B parameters. Our novel training pipeline unifies multiple programming languages and transforms various code-related tasks into a common retrieval framework. Our 7B model sets a new state-of-the-art (SOTA) in code retrieval, outperforming the previous leading model, Voyage-Code, by over 20% on CoIR benchmark.
  arXiv  Detail & Related papers  (2024-11-19T16:54:45Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• A Survey on Evaluating Large Language Models in Code Generation Tasks [30.256255254277914]
  This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks. With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation.
  arXiv  Detail & Related papers  (2024-08-29T12:56:06Z)
• StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback [58.20547418182074]
  We introduce StepCoder, a novel framework for code generation, consisting of two main components. CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks. FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization. Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
  arXiv  Detail & Related papers  (2024-02-02T13:14:31Z)
• Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search [7.822427053078387]
  Generation-Augmented Retrieval (GAR) framework generates exemplar code snippets to augment queries. We propose a simple yet effective method that additionally Rewrites the Code (ReCo) within the for style normalization. Code Style Similarity is the first metric tailored to quantify stylistic similarities in code.
  arXiv  Detail & Related papers  (2024-01-09T12:12:50Z)
• REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models [11.78036105494679]
  This paper introduces a novel code-to-code search technique that enhances the performance of Large Language Models (LLMs) We present the first-ever code search method that encodes dynamic information during training without the need to execute either the corpus under search or the search query at inference time.
  arXiv  Detail & Related papers  (2023-05-05T20:46:56Z)
• Generation-Augmented Query Expansion For Code Retrieval [51.20943646688115]
  We propose a generation-augmented query expansion framework. Inspired by the human retrieval process - sketching an answer before searching. We achieve new state-of-the-art results on the CodeSearchNet benchmark.
  arXiv  Detail & Related papers  (2022-12-20T23:49:37Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• A Transformer-based Approach for Source Code Summarization [86.08359401867577]
  We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
  arXiv  Detail & Related papers  (2020-05-01T23:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.