@inproceedings{lee-etal-2026-kolegalqa,
    title = "{K}o{L}egal{QA}: A {K}orean Legal {QA} Dataset for Trustworthy and Explanation-Grounded Legal {AI}",
    author = "Lee, Yongtae  and
      Lee, Surin  and
      Kim, Sumin  and
      Rahman, S M Wahidur  and
      Lee, Heung-No",
    editor = "Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Krishna, Satyapriya  and
      Das, Anubrata  and
      Dhamala, Jwala  and
      Cao, Yang Trista  and
      Kumarage, Tharindu  and
      Ramakrishna, Anil  and
      Christodoulopoulos, Christos  and
      Wan, Yixin  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.trustnlp-main.13/",
    pages = "240--255",
    ISBN = "979-8-89176-418-7",
    abstract = "Legal QA systems may benefit from training data that is expert-verified and associated with statutory provisions, as fluent generation alone cannot guarantee legally relevant and citation-supported outputs. However, existing Korean legal datasets provide limited support for legal QA and statute-associated response generation. To address this gap, we introduce KoLegalQA, a large-scale Korean legal question{--}answer corpus designed for research on legal QA and explanation-oriented legal response generation in real-world consultation scenarios. The dataset comprises 19k consultations collected from government-operated services, with all responses originally authored or verified by licensed legal professionals. Unlike prior resources, KoLegalQA provides explicit statutory references and clause-level summaries, enabling research on citation-associated and explanation-oriented legal response generation. We benchmark six Korean-capable LLMs using both automated evaluation (G-Eval) and human assessment across multiple criteria, including legal correctness, reasoning quality, and citation relevance. Experimental results show that fine-tuning on KoLegalQA generally improves legal reasoning validity and statute-associated response generation across most evaluated models. We present this resource as a practical benchmark dataset for Korean legal NLP research. Dataset splits, preprocessing scripts, and evaluation code will be publicly released to support reproducible research."
}