KoLegalQA: A Korean Legal QA Dataset for Trustworthy and Explanation-Grounded Legal AI

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 citationsupported 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.