@inproceedings{chen-etal-2025-enhancing-attributed,
    title = "Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning",
    author = "Chen, Yuhan  and
      Zou, Bowei  and
      Fan, Yifan  and
      Chen, Yuchong  and
      Cao, Shujun  and
      Hong, Yu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.420/",
    doi = "10.18653/v1/2025.findings-emnlp.420",
    pages = "7947--7956",
    ISBN = "979-8-89176-335-7",
    abstract = "We study Attributed Question Answering (abbr., AQA), a newly-released long-form answer generation task. The tailored and efficient training programmes haven{'}t yet been leveraged to strengthen AQA models. This hinders the simultaneous enhancement of their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning. To address the issue, we propose a tailored progressive curriculum learning approach, and use it to optimize both encoder-decoder and decoder-only AQA models. Experiments on the benchmark QuoteSum show that our approach yields substantial improvements and enables the AQA performance to reach 73.9{\%} Sem-F1 score."
}