Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models
Joana Ribeiro de Faria, Huiyuan Xie, Felix Steffek

TL;DR
This study evaluates GPT-4's ability to automatically extract key legal information from UK Employment Tribunal judgments, demonstrating high accuracy and potential for legal research and dispute outcome prediction.
Contribution
It is the first comprehensive evaluation of GPT-4 for extracting detailed legal case information from tribunal judgments, with a focus on practical applications.
Findings
GPT-4 achieves high accuracy in extracting legal case details.
The extracted data can be used to predict case outcomes.
Demonstrates the potential of LLMs in legal information processing.
Abstract
Court transcripts and judgments are rich repositories of legal knowledge, detailing the intricacies of cases and the rationale behind judicial decisions. The extraction of key information from these documents provides a concise overview of a case, crucial for both legal experts and the public. With the advent of large language models (LLMs), automatic information extraction has become increasingly feasible and efficient. This paper presents a comprehensive study on the application of GPT-4, a large language model, for automatic information extraction from UK Employment Tribunal (UKET) cases. We meticulously evaluated GPT-4's performance in extracting critical information with a manual verification process to ensure the accuracy and relevance of the extracted data. Our research is structured around two primary extraction tasks: the first involves a general extraction of eight key aspects…
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Taxonomy
TopicsArtificial Intelligence in Law
MethodsAttention Is All You Need · Linear Layer · Layer Normalization · Multi-Head Attention · Softmax · Dropout · Byte Pair Encoding · Absolute Position Encodings · Residual Connection · Position-Wise Feed-Forward Layer
