How does instruction-tuned retrieval performance on multi-hop queries from MuSiQue compare to single-context a

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

How does instruction-tuned retrieval performance on multi-hop queries from MuSiQue compare to single-context adversarial training when evaluated on out-of-domain BEIR subsets (SciFact, TREC-COVID) in terms of MRR@10?

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