@inproceedings{qiang-etal-2025-exploring-knowledge,
    title = "Exploring Knowledge Filtering for Retrieval-Augmented Discriminative Tasks",
    author = "Qiang, Minjie  and
      Wang, Zhongqing  and
      Bao, Xiaoyi  and
      Ma, HaoYuan  and
      Li, Shoushan  and
      Zhou, Guodong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.86/",
    pages = "1716--1729",
    ISBN = "979-8-89176-256-5",
    abstract = "Retrieval-augmented methods have achieved remarkable advancements in alleviating the hallucination of large language models.Nevertheless, the introduction of external knowledge does not always lead to the expected improvement in model performance, as irrelevant or harmful information present in the retrieved knowledge can compromise the prediction process.To address these challenges, we propose a novel framework aimed at improving model performance by incorporating knowledge filtering and prediction fusion mechanisms.In particular, our approach first employs a perplexity-based annotation method to collect training data.Then, we design four distinct strategies to filter out harmful retrieved knowledge.Finally, we integrate the filtered knowledge to generate the final result via batch-wise predictions.We conduct extensive experiments across multiple discriminative task datasets to evaluate the proposed framework.The results demonstrate that our framework can significantly enhance the performance of models on discriminative tasks."
}