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            <title>Proceedings of the Natural Legal Language Processing Workshop 2025</title>
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    <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 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 ~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 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, ObliQA-MP and our validation and RAG systems offer a stronger benchmark and a practical recipe for grounded, citation-controlled QA in regulatory domains.</abstract>
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%0 Conference Proceedings
%T Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG
%A Gokhan, Tuba
%A Briscoe, Ted
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-338-8
%F gokhan-briscoe-2025-grounded
%X 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 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 ~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 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, ObliQA-MP and our validation and RAG systems offer a stronger benchmark and a practical recipe for grounded, citation-controlled QA in regulatory domains.
%U https://aclanthology.org/2025.nllp-1.10/
%P 135-146

Markdown (Informal)

[Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG](https://aclanthology.org/2025.nllp-1.10/) (Gokhan & Briscoe, NLLP 2025)

• Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG (Gokhan & Briscoe, NLLP 2025)

ACL

• Tuba Gokhan and Ted Briscoe. 2025. Grounded Answers from Multi-Passage Regulations: Learning-to-Rank for Regulatory RAG. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 135–146, Suzhou, China. Association for Computational Linguistics.