LLM-Independent Adaptive RAG: Let the Question Speak for Itself
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.
681c8f10822d05bba75de266
Maria Marina
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Nikolay Ivanov
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2025-05-09T07:22:18.868Z
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Ivanov
user
VirVen
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Sergey Pletenev
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Mikhail Salnikov
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2025-05-08T11:01:37.065Z
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Mikhail Salnikov
user
msalnikov
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Daria Galimzianova
681c8f10822d05bba75de26b
Nikita Krayko
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Vasily Konovalov
claimed_verified
2025-05-12T06:51:09.438Z
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Vasily Konovalov
user
Vasily
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Alexander Panchenko
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Viktor Moskvoretskii
claimed_verified
2025-05-09T07:22:16.363Z
63bbfd74141c7d395c471768
https://cdn-avatars.huggingface.co/v1/production/uploads/1673264437106-noauth.jpeg
Viktor Moskvoretskii
user
VityaVitalich
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
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.
Lightweight LLM-independent adaptive retrieval methods using external information achieve efficient and effective QA performance.
Large Language Models LLMs hallucinations Retrieval-Augmented Generation RAG adaptive retrieval uncertainty estimation QA datasets question answering