@inproceedings{kim-etal-2025-unirag,
    title = "{U}ni{RAG}: A Unified {RAG} Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting",
    author = "Kim, Gun Il  and
      Kim, Jong Wook  and
      Jang, Beakcheol",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1022/",
    doi = "10.18653/v1/2025.findings-emnlp.1022",
    pages = "18795--18810",
    ISBN = "979-8-89176-335-7",
    abstract = "Knowledge-intensive queries require accurate answers that are explicitly grounded in retrieved evidence. However, existing retrieval-augmented generation (RAG) approaches often struggle with query complexity, suffer from propagated reasoning errors, or rely on incomplete or noisy retrieval, limiting their effectiveness. To address these limitations, we introduce UniRAG, a unified RAG framework that integrates entity-grounded query decomposition, break-down reasoning, and iterative query rewriting. Specifically, UniRAG decomposes queries into semantically coherent sub-queries, explicitly verifies retrieved sub-facts through a dedicated reasoning module, and adaptively refines queries based on identified knowledge gaps, significantly improving answer completeness and reliability. Extensive benchmark evaluations on complex question-answering datasets, including multi-hop HotPotQA and 2WikiMultihopQA, biomedical MedMCQA and MedQA, and fact-verification FEVER and SciFact, demonstrate that UniRAG consistently achieves performance improvements across various state-of-the-art LLMs, such as LLaMA-3.1-8B, GPT-3.5-Turbo, and Gemini-1.5-Flash."
}