Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriev

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 goal: How does retrieval latency scale with the number of hops in multi-hop RAG queries when comparing dense retriever (e.g., DPR) vs. sparse retriever (e.g., BM25) on HotPotQA and MuSiQue under adversarial context perturbations?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.