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

• URL: http://arxiv.org/abs/2505.04253v1
• Date: Wed, 07 May 2025 08:58:52 GMT
• Title: LLM-Independent Adaptive RAG: Let the Question Speak for Itself
• Authors: Maria Marina, Nikolay Ivanov, Sergey Pletenev, Mikhail Salnikov, Daria Galimzianova, Nikita Krayko, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii,
• Abstract summary: Large Language Models (LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps this, but at a high computational cost while risking misinformation.<n>In this study, we introduce lightweight LLM-independent adaptive retrieval methods based on external information.
• Score: 47.60917219813637
• License: http://creativecommons.org/licenses/by/4.0/
• 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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