Summary of The Clc-uket Dataset: Benchmarking Case Outcome Prediction For the Uk Employment Tribunal, by Huiyuan Xie et al.
The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal
by Huiyuan Xie, Felix Steffek, Joana Ribeiro de Faria, Christine Carter, Jonathan Rutherford
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper develops a benchmark for predicting case outcomes in the UK Employment Tribunal (UKET) by employing a large language model (LLM) for automatic annotation. A comprehensive legal dataset, CLC-UKET, is created, consisting of approximately 19,000 UKET cases and their metadata. The study examines a multi-class case outcome prediction task in the UKET, with finetuned transformer models outperforming zero-shot and few-shot LLMs. The performance of zero-shot LLMs can be enhanced by integrating task-related information into few-shot examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to predict the outcomes of cases in the UK Employment Tribunal (UKET) using artificial intelligence. It makes a big dataset with lots of information about these cases, which will help people working on this problem. The study looks at how well different models can do this task and finds that some models are better than others. This could be useful for helping resolve employment disputes. |
Keywords
» Artificial intelligence » Few shot » Large language model » Transformer » Zero shot
