mlsanityData to 7 Oct 2026

A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science

arXiv 2412.15404 ↗First posted 19 Dec 2024cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

In the rapidly evolving field of data science, efficiently navigating the expansive body of academic literature is crucial for informed decision-making and innovation. This paper presents an enhanced Retrieval-Augmented Generation (RAG) application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources. The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting bibliographic information, fine-tuned embedding models, semantic chunking, and an abstract-first retrieval method, to significantly improve the relevance and accuracy of the retrieved information. This implementation of AI specifically addresses the challenge of academic literature navigation. A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics, particularly Context Relevance, underscoring the system's effectiveness in reducing information overload and enhancing decision-making processes. Our findings highlight the potential of this enhanced Retrieval-Augmented Generation system to transform academic exploration within data science, ultimately advancing the workflow of research and innovation in the field.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 42 references that are in the map. First posted on arXiv 19 Dec 2024.

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18 of 42 references.

Closest ancestors · 2

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  1. 2024Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA22 Jul
  2. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 16

  1. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  2. 2023Enabling Large Language Models to Generate Text with Citationscited by 584
  3. 2023Textbooks Are All You Needcited by 561
  4. 2023Large Language Models Understand and Can be Enhanced by Emotional Stimulicited by 143
  5. 2024The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performancecited by 97
  6. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 89
  7. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
  8. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 37
  9. 2022Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge networkcited by 34
  10. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answeringcited by 23
  11. 2023GAR-meets-RAG Paradigm for Zero-Shot Information Retrievalcited by 18
  12. 2023GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasetscited by 12
4 more
  1. 2024Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Studycited by 11
  2. 2024CiteAssist: A System for Automated Preprint Citation and BibTeX Generationcited by 11
  3. 2018Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parserscited by 2
  4. 2023KNIMEZoBot: Enhancing Literature Review with Zotero and KNIME OpenAI Integration using Retrieval-Augmented Generationcited by 2

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  1. 2024Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research
  2. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
  3. 2025Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach
  4. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges
  5. 2024Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview
  6. 2026Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
  7. 2024Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
  8. 2025The Budget AI Researcher and the Power of RAG Chains