Comparative F1 Score Degradation of Multi-Hop Retrievers on MuSiQue and HotpotQA Under Synthetic Noise Injection
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 goal: How does the F1 score degradation of multi-hop retrievers compare between MuSiQue and HotpotQA under synthetic noise injection? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.
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