@inproceedings{xin-etal-2025-aligning,
    title = "Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering",
    author = "Xin, Chunlei  and
      Zhou, Shuheng  and
      Chen, Xuanang  and
      Lu, Yaojie  and
      Zhu, Huijia  and
      Wang, Weiqiang  and
      Liu, Zhongyi  and
      Han, Xianpei  and
      Sun, Le",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2025.coling-main.67/",
    pages = "1000--1012",
    abstract = "Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader`s needs. Existing retrieval methods aim to gather relevant passages but often fail to prioritize consistent and useful information for the reader. In this paper, we introduce a novel Reader-Centered Passage Selection (R-CPS) method, which enhances the performance of the retrieve-then-read pipeline by re-ranking and clustering passages from the reader`s perspective. Our method re-ranks passages based on the reader`s prediction probability distribution and clusters passages according to the predicted answers, prioritizing more useful and relevant passages to the top and reducing inconsistent information. Experiments on ODQA datasets demonstrate the effectiveness of our approach in improving the quality of evidence passages under zero-shot settings."
}