Retrieval Method Impact on RAG Accuracy-Throughput Trade-offs in Domain-Specific Benchmarks

Abstract

Abstract: Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy becomes increasingly challenging as the corpus of documents scales up, with Retrievers playing an outsized role in the overall RAG accuracy by extracting the most relevant document from the corpus to provide context to the LLM. In this paper, we propose the 'Blended RAG' method of leveraging semantic search techniques, such as Dense Vector indexes and Sparse Encoder indexes, ble

Research Question

How does the accuracy-throughput trade-off in RAG systems vary when using different retrieval methods (e.g., dense vs. sparse) across model sizes (1B-7B) on domain-specific benchmarks like MedQA?

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