How does the inference throughput (queries per second) of Vendi-RAG compare to standard dense retriever RAG ba

Abstract

Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) for domain-specific question-answering (QA) tasks by leveraging external knowledge sources. However, traditional RAG systems primarily focus on relevance-based retrieval and often struggle with redundancy, especially when reasoning requires connecting information from multiple sources. This paper introduces Vendi-RAG, a framework based on an iterative process that jointly optimizes retrieval diversity and answer quality. This joint optimization leads to significantly higher accuracy for multi-hop QA tasks. Vendi-RAG lev

Research Question

How does the inference throughput (queries per second) of Vendi-RAG compare to standard dense retriever RAG baselines on HotPotQA when scaling the number of multi-hop reasoning steps?

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