title: LLM-Independent Adaptive RAG: Let the Question Speak for Itself
summary: Large Language Models~(LLMs) are prone to hallucinations, and
  Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high
  computational cost while risking misinformation. Adaptive retrieval aims to
  retrieve only when necessary, but existing approaches rely on LLM-based
  uncertainty estimation, which remain inefficient and impractical. In this
  study, we introduce lightweight LLM-independent adaptive retrieval methods
  based on external information. We investigated 27 features, organized into 7
  groups, and their hybrid combinations. We evaluated these methods on 6 QA
  datasets, assessing the QA performance and efficiency. The results show that
  our approach matches the performance of complex LLM-based methods while
  achieving significant efficiency gains, demonstrating the potential of external
  information for adaptive retrieval.
paper_authors._id: 681c8f10822d05bba75de266
paper_authors.name: Maria Marina
paper_authors._id: 681c8f10822d05bba75de267
paper_authors.name: Nikolay Ivanov
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paper_authors.fullname: Ivanov
paper_authors.type: user
paper_authors.user: VirVen
paper_authors._id: 681c8f10822d05bba75de268
paper_authors.name: Sergey Pletenev
paper_authors._id: 681c8f10822d05bba75de269
paper_authors.name: Mikhail Salnikov
paper_authors.status: extracted_pending
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paper_authors.avatarUrl: /avatars/fd92ae2986d435a47eb1e382ac11d8e0.svg
paper_authors.fullname: Mikhail Salnikov
paper_authors.type: user
paper_authors.user: msalnikov
paper_authors._id: 681c8f10822d05bba75de26a
paper_authors.name: Daria Galimzianova
paper_authors._id: 681c8f10822d05bba75de26b
paper_authors.name: Nikita Krayko
paper_authors._id: 681c8f10822d05bba75de26c
paper_authors.name: Vasily Konovalov
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/842719c100a5969be75d04da97333675.svg
paper_authors.fullname: Vasily Konovalov
paper_authors.type: user
paper_authors.user: Vasily
paper_authors._id: 681c8f10822d05bba75de26d
paper_authors.name: Alexander Panchenko
paper_authors._id: 681c8f10822d05bba75de26e
paper_authors.name: Viktor Moskvoretskii
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-09T07:22:16.363Z
paper_authors._id: 63bbfd74141c7d395c471768
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673264437106-noauth.jpeg
paper_authors.fullname: Viktor Moskvoretskii
paper_authors.type: user
paper_authors.user: VityaVitalich
paper_title: LLM-Independent Adaptive RAG: Let the Question Speak for Itself
paper_summary: Large Language Models~(LLMs) are prone to hallucinations, and
  Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high
  computational cost while risking misinformation. Adaptive retrieval aims to
  retrieve only when necessary, but existing approaches rely on LLM-based
  uncertainty estimation, which remain inefficient and impractical. In this
  study, we introduce lightweight LLM-independent adaptive retrieval methods
  based on external information. We investigated 27 features, organized into 7
  groups, and their hybrid combinations. We evaluated these methods on 6 QA
  datasets, assessing the QA performance and efficiency. The results show that
  our approach matches the performance of complex LLM-based methods while
  achieving significant efficiency gains, demonstrating the potential of external
  information for adaptive retrieval.
paper_ai_summary: Lightweight LLM-independent adaptive retrieval methods using external information achieve efficient and effective QA performance.
paper_ai_keywords: Large Language Models LLMs hallucinations Retrieval-Augmented Generation RAG adaptive retrieval uncertainty estimation QA datasets question answering