@inproceedings{chen-etal-2025-comrag,
    title = "{C}om{RAG}: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry",
    author = "Chen, Qinwen  and
      Tao, Wenbiao  and
      Zhu, Zhiwei  and
      Xi, Mingfan  and
      Guo, Liangzhong  and
      Wang, Yuan  and
      Wang, Wei  and
      Lan, Yunshi",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.53/",
    pages = "749--763",
    ISBN = "979-8-89176-288-6",
    abstract = "Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines{---}achieving up to 25.9{\%} improvement in vector similarity, reducing latency by 8.7{\%}{--}23.3{\%}, and lowering chunk growth from 20.23{\%} to 2.06{\%} over iterations."
}