CLC-UKET数据集：英国就业法庭案件结果预测基准
The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal
AI总结：
本文构建含约19,000件英国就业法庭案件的CLC-UKET数据集，并建立多类别结果预测基准，实验显示微调transformer优于零样本和少样本LLM。
摘要：
本文探讨技术创新与司法可及性的交叉领域，为预测英国就业法庭（UKET）的案件结果开发了一个基准。为应对大量人工标注的挑战，本研究采用大型语言模型（LLM）进行自动标注，从而创建了CLC-UKET数据集。该数据集包含约19,000件UKET案件及其元数据。全面的法律标注涵盖事实、诉求、判例引用、成文法引用、案件结果、理由和司法管辖区代码。借助CLC-UKET数据，我们考察了UKET中的一项多类别案件结果预测任务。研究收集了人类预测结果，以建立用于模型比较的性能参照。基线模型的实证结果表明，在UKET预测任务上，经过微调的transformer模型优于零样本和少样本LLM。通过将任务相关信息整合到少样本示例中，可以提升零样本LLM的性能。我们希望CLC-UKET数据集连同人工标注和实证发现，能够成为就业相关争议解决的有价值基准。
英文摘要：
This paper explores the intersection of technological innovation and access to justice by developing a benchmark for predicting case outcomes in the UK Employment Tribunal (UKET). To address the challenge of extensive manual annotation, the study employs a large language model (LLM) for automatic annotation, resulting in the creation of the CLC-UKET dataset. The dataset consists of approximately 19,000 UKET cases and their metadata. Comprehensive legal annotations cover facts, claims, precedent references, statutory references, case outcomes, reasons and jurisdiction codes. Facilitated by the CLC-UKET data, we examine a multi-class case outcome prediction task in the UKET. Human predictions are collected to establish a performance reference for model comparison. Empirical results from baseline models indicate that finetuned transformer models outperform zero-shot and few-shot LLMs on the UKET prediction task. The performance of zero-shot LLMs can be enhanced by integrating task-related information into few-shot examples. We hope that the CLC-UKET dataset, along with human annotations and empirical findings, can serve as a valuable benchmark for employment-related dispute resolution.
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