clarin-pl/polichat-rag-evaluation

Dataset used for RAG evaluation in the following publication:

PoliChat: Retrieval Augmented Generation on University Documents and Regulations.
Wojtasik, K., Berdowski, A., Okulska, I., Piasecki, M. (2025).  
In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds) Computational Science – ICCS 2025 Workshops. ICCS 2025. Lecture Notes in Computer Science, vol 15911. Springer, Cham. 
https://doi.org/10.1007/978-3-031-97570-7_21

Citation:

@InProceedings{10.1007/978-3-031-97570-7_21,
author="Wojtasik, Konrad
and Berdowski, Adrian
and Okulska, Inez
and Piasecki, Maciej",
editor="Paszynski, Maciej
and Barnard, Amanda S.
and Zhang, Yongjie Jessica",
title="PoliChat: Retrieval Augmented Generation on University Documents and Regulations",
booktitle="Computational Science -- ICCS 2025 Workshops",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="273--288",
abstract="University regulations are often complex and difficult to navigate. To address this, we developed PoliChat, a Retrieval-Augmented Generation (RAG)-based chatbot that provides accurate and transparent access to regulatory information. Validated at Wroc{\l}aw University of Science and Technology, PoliChat integrates real-time retrieval with citation mechanisms to enhance reliability. As part of our research, we prepared and annotated a dataset of university regulations to evaluate information retrieval and answer generation performance. We examine key factors that affect RAG performance in regulatory domains, including model size, document length, summarization, retrieved context size, and prompting strategies. We introduce Analyze{\&}Answer, a prompting method that improves response coherence and citation accuracy.",
isbn="978-3-031-97570-7"
}