LLM-Independent Adaptive RAG: Let the Question Speak for Itself

Maria Marina; Nikolay Ivanov; Sergey Pletenev; Mikhail Salnikov; Daria Galimzianova; Nikita Krayko; Vasily Konovalov; Alexander Panchenko; Viktor Moskvoretskii

arXiv · 2505.04253

LLM-Independent Adaptive RAG: Let the Question Speak for Itself

Maria Marina·Nikolay Ivanov·Sergey Pletenev·Mikhail Salnikov·Daria Galimzianova·Nikita Krayko·Vasily Konovalov·Alexander Panchenko·Viktor Moskvoretskii

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Abstract

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.

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Maria Marina, Nikolay Ivanov, Sergey Pletenev, Mikhail Salnikov, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii. 2025-05-07. LLM-Independent Adaptive RAG: Let the Question Speak for Itself. https://arxiv.org/abs/2505.04253

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