This paper introduces a Korean legal judgment prediction (LJP) dataset for
insurance disputes. Successful LJP models on insurance disputes can benefit
insurance companies and their customers. It can save both sides' time and money
by allowing them to predict how the result would come out if they proceed to
the dispute mediation process. As is often the case with low-resource
languages, there is a limitation on the amount of data available for this
specific task. To mitigate this issue, we investigate how one can achieve a
good performance despite the limitation in data. In our experiment, we
demonstrate that Sentence Transformer Fine-tuning (SetFit, Tunstall et al.,
2022) is a good alternative to standard fine-tuning when training data are
limited. The models fine-tuned with the SetFit approach on our data show
similar performance to the Korean LJP benchmark models (Hwang et al., 2022)
despite the much smaller data size.

本文介绍了一个用于保险争议的韩国法律判决预测（LJP）数据集。通过在数据有限的情况下使用 SetFit 方法进行训练，我们实验证明该方法在性能上与韩国 LJP 基准模型相似，尽管数据规模较小。