@inproceedings{kalra-etal-2025-mor,
    title = "{M}o{R}: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers",
    author = "Kalra, Jushaan Singh  and
      Zhao, Xinran  and
      Kim, To Eun  and
      Cai, Fengyu  and
      Diaz, Fernando  and
      Wu, Tongshuang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.601/",
    pages = "11982--12001",
    ISBN = "979-8-89176-332-6",
    abstract = "Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals: BM25 captures lexical matches; dense retrievers, semantic similarity. Yet in practice, we typically fix a single retriever based on heuristics, which fails to generalize across diverse information needs. Can we dynamically select and integrate multiple retrievers for each individual query, without the need for manual selection? In our work, we validate this intuition with quantitative analysis and introduce a mixture of retrievers: a zero-shot, weighted combination of heterogeneous retrievers. Extensive experiments show that such mixtures are effective and efficient: Despite totaling just 0.8B parameters, this mixture outperforms every individual retriever and even larger 7B models{---}by +10.8{\%} and +3.9{\%} on average, respectively. Further analysis also shows that this mixture framework can help incorporate specialized non-oracle human information sources as retrievers to achieve good collaboration, with a 58.9{\%} relative performance improvement over simulated humans alone."
}