The recent advances of deep learning have dramatically changed how machine
learning, especially in the domain of natural language processing, can be
applied to legal domain. However, this shift to the data-driven approaches
calls for larger and more diverse datasets, which are nevertheless still small
in number, especially in non-English languages. Here we present the first
large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of
one legal corpus, two classification tasks, two legal judgement prediction
(LJP) tasks, and one summarization task. The legal corpus consists of 147k
Korean precedents (259M tokens), of which 63k are sentenced in last 4 years and
96k are from the first and the second level courts in which factual issues are
reviewed. The two classification tasks are case names (11.3k) and statutes
(2.8k) prediction from the factual description of individual cases. The LJP
tasks consist of (1) 10.5k criminal examples where the model is asked to
predict fine amount, imprisonment with labor, and imprisonment without labor
ranges for the given facts, and (2) 4.7k civil examples where the inputs are
facts and claim for relief and outputs are the degrees of claim acceptance. The
summarization task consists of the Supreme Court precedents and the
corresponding summaries (20k). We also release realistic variants of the
datasets by extending the domain (1) to infrequent case categories in case name
(31k examples) and statute (17.7k) classification tasks, and (2) to long input
sequences in the summarization task (51k). Finally, we release LCUBE, the first
Korean legal language model trained on the legal corpus from this study. Given
the uniqueness of the Law of South Korea and the diversity of the legal tasks
covered in this work, we believe that LBOX OPEN contributes to the
multilinguality of global legal research. LBOX OPEN and LCUBE will be publicly
available.

本论文介绍了韩国法律 AI 数据集 LBOX OPEN，该数据集包含了韩国判例文本、案例分类、法规预测、判决预测和摘要生成等多种任务，并发布了第一个基于该数据集训练的韩国法律语言模型 LCUBE，旨在为全球法律研究的多语种发展做出贡献。