@inproceedings{hu-etal-2025-beyond,
    title = "Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering",
    author = "Hu, Zhanghao  and
      Yan, Hanqi  and
      Zhu, Qinglin  and
      Shen, Zhenyi  and
      He, Yulan  and
      Gui, Lin",
    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.981/",
    pages = "19975--19990",
    ISBN = "979-8-89176-251-0",
    abstract = "Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new frontiers. However, prevailing retriever{--}reader pipelines often depend on multiple rounds of prompt-level instructions, leading to high computational overhead, instability, and suboptimal retrieval coverage. In this paper, we propose EmbQA, an embedding-level framework that alleviates these shortcomings by enhancing both the retriever and the reader. Specifically, we refine query representations via lightweight linear layers under an unsupervised contrastive learning objective, thereby reordering retrieved passages to highlight those most likely to contain correct answers. Additionally, we introduce an exploratory embedding that broadens the model{'}s latent semantic space to diversify candidate generation and employs an entropy-based selection mechanism to choose the most confident answer automatically. Extensive experiments across three open-source LLMs, three retrieval methods, and four ODQA benchmarks demonstrate that EmbQA substantially outperforms recent baselines in both accuracy and efficiency."
}