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        <title>NUST Nova at RIRAG 2025: A Hybrid Framework for Regulatory Information Retrieval and Question Answering</title>
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            <title>Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)</title>
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    <abstract>NUST Nova participates in RIRAG Shared Task, addressing two critical challenges: Task 1 involves retrieving relevant subsections from regulatory documents based on user queries, while Task 2 focuses on generating concise, contextually accurate answers using the retrieved information. We propose a Hybrid Retrieval Framework that combines graph-based retrieval, vector-based methods, and keyword matching BM25 to enhance relevance and precision in regulatory QA. Using score-based fusion and iterative refinement, the framework retrieves the top 10 relevant passages, which are then used by an LLM to generate accurate, context-aware answers. After empirical evaluation, we also conduct an error analysis to identify our framework‘s limitations.</abstract>
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%0 Conference Proceedings
%T NUST Nova at RIRAG 2025: A Hybrid Framework for Regulatory Information Retrieval and Question Answering
%A Khan, Mariam Babar
%A Ameer, Huma
%A Latif, Seemab
%A Fatima, Mehwish
%Y Gokhan, Tuba
%Y Wang, Kexin
%Y Gurevych, Iryna
%Y Briscoe, Ted
%S Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F khan-etal-2025-nust
%X NUST Nova participates in RIRAG Shared Task, addressing two critical challenges: Task 1 involves retrieving relevant subsections from regulatory documents based on user queries, while Task 2 focuses on generating concise, contextually accurate answers using the retrieved information. We propose a Hybrid Retrieval Framework that combines graph-based retrieval, vector-based methods, and keyword matching BM25 to enhance relevance and precision in regulatory QA. Using score-based fusion and iterative refinement, the framework retrieves the top 10 relevant passages, which are then used by an LLM to generate accurate, context-aware answers. After empirical evaluation, we also conduct an error analysis to identify our framework‘s limitations.
%U https://aclanthology.org/2025.regnlp-1.11/
%P 73-78

Markdown (Informal)

[NUST Nova at RIRAG 2025: A Hybrid Framework for Regulatory Information Retrieval and Question Answering](https://aclanthology.org/2025.regnlp-1.11/) (Khan et al., RegNLP 2025)

• NUST Nova at RIRAG 2025: A Hybrid Framework for Regulatory Information Retrieval and Question Answering (Khan et al., RegNLP 2025)

ACL

• Mariam Babar Khan, Huma Ameer, Seemab Latif, and Mehwish Fatima. 2025. NUST Nova at RIRAG 2025: A Hybrid Framework for Regulatory Information Retrieval and Question Answering. In Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025), pages 73–78, Abu Dhabi, UAE. Association for Computational Linguistics.