A synergistic multi-stage RAG architecture for boosting context relevance in data science literature

Navigating the voluminous and rapidly evolving data science literature presents a significant bottleneck for researchers and practitioners. Standard Retrieval-Augmented Generation (RAG) systems often struggle with retrieving precisely relevant context from this dense academic corpus. This paper intr...

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Hlavní autoři:	Ahmet Yasin Aytar, Kamer Kaya, Kemal Kılıç
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2025-12-01
Edice:	Natural Language Processing Journal
Témata:	Retrieval-Augmented Generation (RAG) Data science Literature retrieval Academic insights Large Language Models (LLM)
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S294971912500055X
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