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EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
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Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries. While iterative retrieval methods improve performance by gathering additional information, current approaches often rely on multiple calls of large language models (LLMs). In this paper, we introduce EfficientRAG, an efficient retriever for multi-hop question answering. EfficientRAG iteratively generates new queries without the need for LLM calls at each iteration and filters out irrelevant information. Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.
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Cited by 2 Pith papers
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MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
MINTQA provides 28,366 multi-hop QA pairs across popular/unpopular and old/new knowledge, with sub-questions, and shows that even the best LLMs achieve only about 62% accuracy even with retrieval.
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ComposeRAG: A Modular and Composable RAG for Corpus-Grounded Multi-Hop Question Answering
A modular, verifier-driven RAG pipeline with iterative re-decomposition outperforms fine-tuned and agentic baselines on four multi-hop QA benchmarks.
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