What is the impact of retriever robustness (measured via BEIR) on LLM reasoning accuracy in multi-hop QA tasks

Abstract

Abstract: Zero-shot evaluation of information retrieval (IR) models is often performed using BEIR; a large and heterogeneous benchmark composed of multiple datasets, covering different retrieval tasks across various domains. Although BEIR has become a standard benchmark for the zero-shot setup, its exclusively English content reduces its utility for underrepresented languages in IR, including Dutch. To address this limitation and encourage the development of Dutch IR models, we introduce BEIR-NL by automatically translating the publicly accessible BEIR datasets into Dutch. Using BEIR-NL, we evaluated a

Research Question

What is the impact of retriever robustness (measured via BEIR) on LLM reasoning accuracy in multi-hop QA tasks when using hybrid retrieval methods compared to purely dense or sparse approaches, and how does this trade-off affect inference throughput?

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