@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
%A Laskar, Md Tahmid Rahman
%A Tremblay, Julien Bouvier
%A Fu, Xue-Yong
%A Chen, Cheng
%A Tn, Shashi Bhushan
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F laskar-etal-2025-ai
%X 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.
%U https://aclanthology.org/2025.emnlp-industry.130/
%P 1856-1866
Markdown (Informal)
[AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents](https://aclanthology.org/2025.emnlp-industry.130/) (Laskar et al., EMNLP 2025)
ACL