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The retrieval-augmented generation (RAG) enables retrieval of relevant information from an external knowledge source and allows large language models (LLMs) to answer queries over previously unseen document collections. However, it was demonstrated that traditional RAG applications perform poorly in answering multi-hop questions, which require retrieving and reasoning over multiple elements of supporting evidence. We introduce a new method called Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata to improve the RAG selection of the relevant documents from various sources applicable to the question. While database filtering is specific to a set of questions from a particular domain and format, we found that Multi-Meta-RAG greatly improves the results on the MultiHop-RAG benchmark. The code is available on GitHub .

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Multi-Meta-RAG: Improving RAG for Multi-hop Queries Using Database Filtering with LLM-Extracted Metadata

  • 会议论文

  • 发表日期：

    2025年

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  • Mykhailo Poliakov,
  • Nadiya Shvai

摘要

The retrieval-augmented generation (RAG) enables retrieval of relevant information from an external knowledge source and allows large language models (LLMs) to answer queries over previously unseen document collections. However, it was demonstrated that traditional RAG applications perform poorly in answering multi-hop questions, which require retrieving and reasoning over multiple elements of supporting evidence. We introduce a new method called Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata to improve the RAG selection of the relevant documents from various sources applicable to the question. While database filtering is specific to a set of questions from a particular domain and format, we found that Multi-Meta-RAG greatly improves the results on the MultiHop-RAG benchmark. The code is available on GitHub .