@inproceedings{aushev-etal-2025-ragulator,
    title = "{RAG}ulator: Effective {RAG} for Regulatory Question Answering",
    author = "Aushev, Islam  and
      Kratkov, Egor  and
      Nikolaev, Evgenii  and
      Glinskii, Andrei  and
      Krikunov, Vasilii  and
      Panchenko, Alexander  and
      Konovalov, Vasily  and
      Belikova, Julia",
    editor = "Gokhan, Tuba  and
      Wang, Kexin  and
      Gurevych, Iryna  and
      Briscoe, Ted",
    booktitle = "Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.regnlp-1.18/",
    pages = "114--120",
    abstract = "Regulatory Natural Language Processing (RegNLP) is a multidisciplinary domain focused on facilitating access to and comprehension of regulatory regulations and requirements. This paper outlines our strategy for creating a system to address the Regulatory Information Retrieval and Answer Generation (RIRAG) challenge, which was conducted during the RegNLP 2025 Workshop. The objective of this competition is to design a system capable of efficiently extracting pertinent passages from regulatory texts (ObliQA) and subsequently generating accurate, cohesive responses to inquiries related to compliance and obligations. Our proposed method employs a lightweight BM25 pre-filtering in retrieving relevant passages. This technique efficiently shortlisting candidates for subsequent processing with Transformer-based embeddings, thereby optimizing the use of resources."
}