# A Korean Legal Judgment Prediction Dataset for Insurance Disputes

arXiv: https://arxiv.org/abs/2401.14654 · HTML: https://mlsanity.com/paper/2401.14654/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Improved Regularization and Robustness for Fine-tuning in Neural Networks](https://mlsanity.com/paper/2111.04578/) (2021)
- [A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction](https://mlsanity.com/paper/2206.05224/) (2022)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting](https://mlsanity.com/paper/2107.05192/) (2021)

## Abstract (arXiv)

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.