@inproceedings{laskar-etal-2025-ai,
    title = "{AI} Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational {AI} Agents",
    author = "Laskar, Md Tahmid Rahman  and
      Tremblay, Julien Bouvier  and
      Fu, Xue-Yong  and
      Chen, Cheng  and
      Tn, Shashi Bhushan",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.130/",
    pages = "1856--1866",
    ISBN = "979-8-89176-333-3",
    abstract = "The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problemshas been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer{-}agent conversations to automatically build a knowledge base. Fine{-}tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold{-}start gap in contact centers by achieving above 90{\%} accuracy in answering information{-}seeking questions. This enables immediate deployment of RAG-powered chatbots."
}