@inproceedings{fe8a81183a96441890631fdc5acf8a97,
title = "ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry",
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.",
author = "Qinwen Chen and Wenbiao Tao and Zhiwei Zhu and Mingfan Xi and Liangzhong Guo and Yuan Wang and Wei Wang and Yunshi Lan",
note = "Publisher Copyright: {\textcopyright}2025 Association for Computational Linguistics.; 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 ; Conference date: 27-07-2025 Through 01-08-2025",
year = "2025",
doi = "10.18653/v1/2025.acl-industry.53",
language = "英语",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "749--763",
editor = "Georg Rehm and Yunyao Li",
booktitle = "Industry Track",
address = "澳大利亚",
}