@inproceedings{ammann-etal-2025-question,
    title = "Question Decomposition for Retrieval-Augmented Generation",
    author = "Ammann, Paul J. L.  and
      Golde, Jonas  and
      Akbik, Alan",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.32/",
    pages = "497--507",
    ISBN = "979-8-89176-254-1",
    abstract = "Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, such as \textit{{``}Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?,''} challenge RAG because relevant facts are often distributed across multiple documents rather than co-occurring in one source, making it difficult for standard RAG to retrieve sufficient information. To address this, we propose a RAG pipeline that incorporates question decomposition: (i) an LLM decomposes the original query into sub-questions, (ii) passages are retrieved for each sub-question, and (iii) the merged candidate pool is reranked to improve the coverage and precision of the retrieved evidence. We show that question decomposition effectively assembles complementary documents, while reranking reduces noise and promotes the most relevant passages before answer generation. We evaluate our approach on the MultiHop-RAG and HotpotQA, showing gains in retrieval ($MRR@10: +36.7\%$) and answer accuracy ($F1: +11.6\%$) over standard RAG baselines. The pipeline is a practical, drop-in enhancement requiring no task-specific training or specialized indexing."
}