@inproceedings{zhang-etal-2025-mrag,
    title = "{MRAG}: A Modular Retrieval Framework for Time-Sensitive Question Answering",
    author = "Zhang, Siyue  and
      Xue, Yuxiang  and
      Zhang, Yiming  and
      Wu, Xiaobao  and
      Luu, Anh Tuan  and
      Zhao, Chen",
    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.167/",
    doi = "10.18653/v1/2025.findings-emnlp.167",
    pages = "3080--3118",
    ISBN = "979-8-89176-335-7",
    abstract = "Understanding temporal concepts and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (LLMs). Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integrate LLMs with external knowledge retrieval (i.e., retrieval-augmented generation). However, off-the-shelf retrievers often struggle to identify relevant documents that require intensive temporal reasoning. To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating complex temporal perturbations and gold evidence labels. As anticipated, all existing retrieval methods struggle with these temporal reasoning-intensive questions. We further propose Modular Retrieval (MRAG), a trainless framework that includes three modules: (1) Question Processing that decomposes question into a main content and a temporal constraint; (2) Retrieval and Summarization that retrieves, splits, and summarize evidence passages based on the main content; (3) Semantic-Temporal Hybrid Ranking that scores semantic and temporal relevance separately for each fine-grained evidence. On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy."
}