Report
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
| Title: | LLM-Independent Adaptive RAG: Let the Question Speak for Itself |
|---|---|
| Authors: | Marina, Maria, Ivanov, Nikolay, Pletenev, Sergey, Salnikov, Mikhail, Galimzianova, Daria, Krayko, Nikita, Konovalov, Vasily, Panchenko, Alexander, Moskvoretskii, Viktor |
| Publication Year: | 2025 |
| Collection: | Computer Science |
| Subject Terms: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| Description: | 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. Comment: 11 pages, 5 figures, 2 tables |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2505.04253 |
| Accession Number: | edsarx.2505.04253 |
| Database: | arXiv |
| Description not available. |