What is the trade-off between retrieval diversity and answer accuracy when applying Vendi-RAG to a dense retri

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

What is the trade-off between retrieval diversity and answer accuracy when applying Vendi-RAG to a dense retriever like Contriever on SQuAD subsets, measured via F1 score and redundancy metrics?

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