Impact of Multi-Hop QA Benchmark Choice on RAG Retriever Evaluation via F1 Score Analysis

Abstract

Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge to answer questions more accurately. However, research on evaluating RAG systems-particularly the retriever component-remains limited, as most existing work focuses on single-context retrieval rather than multi-hop queries, where individual contexts may appear irrelevant in isolation but are essential when combined. In this research, we use the HotPotQA, MuSiQue, and SQuAD datasets to simulate a RAG system and compare three LLM-as-judge evaluation strategies, including our proposed Context-Awar

Research Question

Does the choice of multi-hop QA benchmark (HotPotQA vs. MuSiQue vs. SQuAD) significantly affect the evaluation of RAG retriever strategies, and how can this be measured via F1 score differences across datasets?

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