@inproceedings{moskvoretskii-etal-2025-adaptive,
    title = "Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home",
    author = "Moskvoretskii, Viktor  and
      Marina, Maria  and
      Salnikov, Mikhail  and
      Ivanov, Nikolay  and
      Pletenev, Sergey  and
      Galimzianova, Daria  and
      Krayko, Nikita  and
      Konovalov, Vasily  and
      Nikishina, Irina  and
      Panchenko, Alexander",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.319/",
    pages = "6355--6384",
    ISBN = "979-8-89176-251-0",
    abstract = "Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. Recent adaptive retrieval methods integrate LLMs' intrinsic knowledge with external information appealing to LLM self-knowledge, but they often neglect efficiency evaluations and comparisons with uncertainty estimation techniques. We bridge this gap by conducting a comprehensive analysis of 35 adaptive retrieval methods, including 8 recent approaches and 27 uncertainty estimation techniques, across 6 datasets using 10 metrics for QA performance, self-knowledge, and efficiency. Our findings show that uncertainty estimation techniques often outperform complex pipelines in terms of efficiency and self-knowledge, while maintaining comparable QA performance."
}