@inproceedings{petcu-etal-2026-query,
    title = "Query Decomposition for {RAG}: Balancing Exploration-Exploitation",
    author = "Petcu, Roxana  and
      Murray, Kenton  and
      Khashabi, Daniel  and
      Kanoulas, Evangelos  and
      Rijke, Maarten de  and
      Lawrie, Dawn  and
      Duh, Kevin",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.322/",
    pages = "6857--6871",
    ISBN = "979-8-89176-380-7",
    abstract = "Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer. Efficiently selecting informative documents requires balancing a key trade-off: (i) retrieving broadly enough to capture all the relevant material, and (ii) limiting retrieval to avoid excessive noise and computational cost. We formulate query decomposition and document retrieval in an exploitation-exploration setting, where retrieving one document at a time builds a belief about the utility of a given sub-query and informs the decision to continue exploiting or exploring an alternative. We experiment with a variety of bandit learning methods and demonstrate their effectiveness in dynamically selecting the most informative sub-queries. Our main finding is that estimating document relevance using rank information and human judgments yields a 35{\%} gain in document-level precision, 15{\%} increase in {\ensuremath{\alpha}}-nDCG, and better performance on the downstream task of long-form generation. Code is available on GitHub."
}