@inproceedings{huynh-etal-2025-mixlora,
    title = "{M}ix{L}o{RA}-{DSI}: Dynamically Expandable Mixture-of-{L}o{RA} Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora",
    author = "Huynh, Tuan-Luc  and
      Vu, Thuy-Trang  and
      Wang, Weiqing  and
      Le, Trung  and
      Gasevic, Dragan  and
      Li, Yuan-Fang  and
      Do, Thanh-Toan",
    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.20/",
    pages = "380--396",
    ISBN = "979-8-89176-332-6",
    abstract = "Continually updating model-based indexes in generative retrieval with new documents remains challenging, as full retraining is computationally expensive and impractical under resource constraints. We propose MixLoRA-DSI, a novel framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution (OOD)-driven expansion strategy. Instead of allocating new experts for each new corpus, our proposed expansion strategy enables sublinear parameter growth by selectively introducing new experts only when significant number of OOD documents are detected. Experiments on NQ320k and MS MARCO Passage demonstrate that MixLoRA-DSI outperforms full-model update baselines, with minimal parameter overhead and substantially lower training costs."
}