Characterising and Analysing Retrieval-Augmented Generation Techniques

Abstract:The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance. Thus far, several RAG techniques with impressive features and capabilities have been proposed in the literature, making it a herculean task to select the most appropriate RAG techniques for specific problem scenarios. Thus, we propose a conceptual framework comprising main dimensions and sub-dimensions for characterising (defining "what it is") and analysing (defining "how well it does") existing RAG techniques. The conceptual framework was developed through a qualitative analysis of the attributes of RAG techniques reported in the literature. It can serve as a pre-empirical filtering mechanism for researchers and practitioners in selecting the highest-fit RAG candidates that meet specific user and task requirements before empirical benchmarking of RAG techniques, which can be computationally and financially expensive.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2610.23226 [cs.AI]

(or arXiv:2610.23226v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.23226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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