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Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction

Published in ITNG 2025

This paper investigates the performance of diverse RAG and RAG-like architectures through domain adaptation and evaluates their ability to generate accurate and relevant responses grounded in contextual knowledge base, with a focus on reducing hallucinations in specialized domains such as customer service.

Recommended citation: Rakin, S., Shibly, M.A.R., Hossain, Z.M., Akbar, M.M., Khan, Z. (2025). Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction. In: Latifi, S. (eds) The 22nd International Conference on Information Technology-New Generations (ITNG 2025). ITNG 2025. Advances in Intelligent Systems and Computing, vol 1463. Springer, Cham.
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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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