@inproceedings{bao-etal-2025-alleviating,
    title = "Alleviating Performance Degradation Caused by Out-of-Distribution Issues in Embedding-Based Retrieval",
    author = "Bao, Haotong  and
      Zhang, Jianjin  and
      Chen, Qi  and
      Han, Weihao  and
      Zeng, Zhengxin  and
      Chang, Ruiheng  and
      Li, Mingzheng  and
      Sun, Hao  and
      Deng, Weiwei  and
      Sun, Feng  and
      Zhang, Qi",
    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.340/",
    doi = "10.18653/v1/2025.findings-emnlp.340",
    pages = "6418--6427",
    ISBN = "979-8-89176-335-7",
    abstract = "In Embedding Based Retrieval (EBR), Approximate Nearest Neighbor (ANN) algorithms are widely adopted for efficient large-scale search. However, recent studies reveal a query out-of-distribution (OOD) issue, where query and base embeddings follow mismatched distributions, significantly degrading ANN performance. In this work, we empirically verify the generality of this phenomenon and provide a quantitative analysis. To mitigate the distributional gap, we introduce a distribution regularizer into the encoder training objective, encouraging alignment between query and base embeddings. Extensive experiments across multiple datasets, encoders, and ANN indices show that our method consistently improves retrieval performance."
}