Enhancing Code Consistency in AI Research with Large Language Models and Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2502.00611v1
• Date: Sun, 02 Feb 2025 00:35:42 GMT
• Title: Enhancing Code Consistency in AI Research with Large Language Models and Retrieval-Augmented Generation
• Authors: Rajat Keshri, Arun George Zachariah, Michael Boone,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Ensuring that code accurately reflects the algorithms and methods described in research papers is critical for maintaining credibility and fostering trust in AI research. 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. This approach improves the accuracy and comprehensiveness of code implementation verification while contributing to the transparency, explainability, and reproducibility of AI research. By automating the verification process, our system reduces manual effort, enhances research credibility, and ultimately advances the state of the art in code verification.

Related papers

• Scoring Verifiers: Evaluating Synthetic Verification in Code and Reasoning [59.25951947621526]
  We introduce benchmarks designed to evaluate the impact of synthetic verification methods on assessing solution correctness. We analyze synthetic verification methods in standard, reasoning-based, and reward-based LLMs. Our results show that recent reasoning models significantly improve test case generation and that scaling test cases enhances verification accuracy.
  arXiv  Detail & Related papers  (2025-02-19T15:32:11Z)
• Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs [54.309127753635366]
  We present the results of a replication study in which we investigate GPT-4 effectiveness in recommending and suggesting idiomatic actions. Our findings underscore the potential of LLMs to achieve tasks where, in the past, implementing recommenders based on complex code analyses was required.
  arXiv  Detail & Related papers  (2025-01-28T15:41:54Z)
• From Scientific Texts to Verifiable Code: Automating the Process with Transformers [2.536225150399618]
  transformers can read research papers that propose algorithms with formal proofs and translate these proofs into verifiable code. We argue that this approach can significantly reduce the barrier to formal verification.
  arXiv  Detail & Related papers  (2025-01-09T14:03:35Z)
• 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)
• An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We? [8.0988059417354]
  We propose a range of approaches to improve the performance of AI-generated code detection. Our best model outperforms state-of-the-art AI-generated code detector (GPTSniffer) and achieves an F1 score of 82.55.
  arXiv  Detail & Related papers  (2024-11-06T22:48:18Z)
• CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers [0.0]
  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.
  arXiv  Detail & Related papers  (2024-08-23T20:51:04Z)
• RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation [54.707460684650584]
  Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG) RAGLAB is a modular and research-oriented open-source library that reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms.
  arXiv  Detail & Related papers  (2024-08-21T07:20:48Z)
• 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)
• 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.