@inproceedings{zhang-etal-2025-multi-level,
    title = "Multi-level Relevance Document Identifier Learning for Generative Retrieval",
    author = "Zhang, Fuwei  and
      Liu, Xiaoyu  and
      Jia, Xinyu  and
      Zhang, Yingfei  and
      Zhang, Shuai  and
      Li, Xiang  and
      Zhuang, Fuzhen  and
      Lin, Wei  and
      Zhang, Zhao",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.497/",
    pages = "10066--10080",
    ISBN = "979-8-89176-251-0",
    abstract = "Generative Retrieval (GR) introduces a new information retrieval paradigm that directly generates unique document identifiers (DocIDs). The key challenge of GR lies in creating effective yet discrete DocIDs that preserve semantic relevance for similar documents while differentiating dissimilar ones. However, existing methods generate DocIDs solely based on the textual content of documents, which may result in DocIDs with weak semantic connections for similar documents due to variations in expression. Therefore, we propose using queries as a bridge to connect documents with varying relevance levels for learning improved DocIDs. In this paper, we propose **M**ulti-l**E**vel **R**elevance document identifier learning for **G**enerative r**E**trieval (MERGE), a novel approach that utilizes multi-level document relevance to learn high-quality DocIDs. MERGE incorporates three modules: a multi-relevance query-document alignment module to effectively align document representations with related queries, an outer-level contrastive learning module to capture binary-level relevance, and an inner-level multi-level relevance learning module to distinguish documents with different relevance levels. Our approach encodes rich hierarchical semantic information and maintains uniqueness across documents. Experimental results on real-world multilingual e-commerce search datasets demonstrate that MERGE significantly outperforms existing methods, underscoring its effectiveness. The source code is available at {\ensuremath{<}}https://github.com/zhangfw123/MERGE{\ensuremath{>}}."
}