@inproceedings{gokhan-briscoe-2025-grounded,
    title = "Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory {RAG}",
    author = "Gokhan, Tuba  and
      Briscoe, Ted",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nllp-1.10/",
    pages = "135--146",
    ISBN = "979-8-89176-338-8",
    abstract = "Regulatory compliance questions often require aggregating evidence from multiple, interrelated sections of long, complex documents. To support question-answering (QA) in this setting, we introduce \textbf{ObliQA-MP}, a dataset for multi-passage regulatory QA, extending the earlier ObliQA benchmark (CITATION), and improve evidence quality with an LLM{--}based validation step that filters out {\textasciitilde}20{\%} of passages missed by prior natural language inference (NLI) based filtering. Our benchmarks show a notable performance drop from single- to multi-passage retrieval, underscoring the challenges of semantic overlap and structural complexity in regulatory texts. To address this, we propose a \textbf{feature-based learning-to-rank (LTR)} framework that integrates lexical, semantic, and graph-derived information, achieving consistent gains over dense and hybrid baselines. We further add a lightweight score-based filter to trim noisy tails and an obligation-centric prompting technique. On ObliQA-MP, LTR improves retrieval (Recall@10/MAP@10/nDCG@10) over dense, hybrid, and fusion baselines. Our generation approach, based on domain-specific filtering plus prompting, achieves strong scores using the RePAS metric (CITATION) on ObliQA-MP, producing faithful, citation-grounded answers. Together, \textbf{ObliQA-MP} and our validation and RAG systems offer a stronger benchmark and a practical recipe for grounded, citation-controlled QA in regulatory domains."
}