@inproceedings{marina-etal-2025-llm,
    title = "{LLM}-Independent Adaptive {RAG}: Let the Question Speak for Itself",
    author = "Marina, Maria  and
      Ivanov, Nikolay  and
      Pletenev, Sergey  and
      Salnikov, Mikhail  and
      Galimzianova, Daria  and
      Krayko, Nikita  and
      Konovalov, Vasily  and
      Panchenko, Alexander  and
      Moskvoretskii, Viktor",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.439/",
    pages = "8708--8720",
    ISBN = "979-8-89176-332-6",
    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 remains 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."
}