@inproceedings{wang-etal-2025-qaencoder,
    title = "{QAE}ncoder: Towards Aligned Representation Learning in Question Answering Systems",
    author = "Wang, Zhengren  and
      Yu, Qinhan  and
      Wei, Shida  and
      Li, Zhiyu  and
      Xiong, Feiyu  and
      Wang, Xiaoxing  and
      Niu, Simin  and
      Liang, Hao  and
      Zhang, Wentao",
    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.217/",
    pages = "4306--4332",
    ISBN = "979-8-89176-251-0",
    abstract = "Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. We introduce QAEncoder, a training-free approach to bridge this gap. Specifically, QAEncoder estimates the expectation of potential queries in the embedding space as a robust surrogate for the document embedding, and attaches document fingerprints to effectively distinguish these embeddings. Extensive experiments across diverse datasets, languages, and embedding models confirmed QAEncoder{'}s alignment capability, which offers a simple-yet-effective solution with zero additional index storage, retrieval latency, training costs, or catastrophic forgetting and hallucination issues. The repository is publicly available at https://github.com/IAAR-Shanghai/QAEncoder."
}