@inproceedings{yao-etal-2025-expandr,
    title = "{E}xpand{R}: Teaching Dense Retrievers Beyond Queries with {LLM} Guidance",
    author = "Yao, Sijia  and
      Huang, Pengcheng  and
      Liu, Zhenghao  and
      Gu, Yu  and
      Yan, Yukun  and
      Yu, Shi  and
      Yu, Ge",
    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.963/",
    pages = "19047--19065",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the retriever as separate modules, overlooking the alignment between generation and ranking objectives. In this work, we propose ExpandR, a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. ExpandR employs the LLM to generate semantically rich query expansions, which are leveraged to enhance the retriever{'}s training. Simultaneously, the LLM is trained using Direct Preference Optimization (DPO), guided by a carefully designed reward function that balances retrieval effectiveness and generation consistency. This joint optimization paradigm enables mutual adaptation between the LLM and the retriever, resulting in query expansions that are both informative and well-suited for retrieval. Experimental results on multiple benchmarks show that ExpandR consistently outperforms strong baselines, achieving more than a 5{\%} improvement in retrieval performance. All codes are available at https://github.com/NEUIR/ExpandR."
}