Wiping out the limitations of Large Language Models - A Taxonomy for Retrieval Augmented Generation
Type
journal article
Date Issued
2024
Author(s)
Research Team
IWI6
Abstract
Current research on RAGs is distributed across various disciplines, and since the technology is evolving very quickly, its unit of analysis is mostly on technological innovations, rather than applications in business contexts. Thus, in this research, we aim to create a taxonomy to conceptualize a comprehensive overview of the constituting characteristics that define RAG applications, facilitating the adoption of this technology in the IS community. To the best of our knowledge, no RAG application taxonomies have been developed so far. We describe our methodology for developing the taxonomy, which includes the criteria for selecting papers, an explanation of our rationale for employing a Large Language Model (LLM)-supported approach to extract and identify initial characteristics, and a concise overview of our systematic process for conceptualizing the taxonomy. Our systematic taxonomy development process includes four iterative phases designed to refine and enhance our understanding and presentation of RAG’s core dimensions. We have developed a total of five meta-dimensions and sixteen dimensions to comprehensively capture the concept of Retrieval-Augmented Generation (RAG) applications. When discussing our findings, we also detail the specific research areas and pose key research questions to guide future information system researchers as they explore the emerging topics of RAG systems.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Pages
16
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
JML_978.pdf
Size
468.43 KB
Format
Adobe PDF
Checksum (MD5)
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