Information extraction from employment tribunal judgments using a large language model

Abstract Transcripts of court judgments are rich repositories of legal knowledge, detailing the intricacies of cases and the rationale behind judicial decisions. Key information extracted 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 information extraction from judgments of the UK Employment Tribunal (UKET). Through a manual verification process, we meticulously evaluated GPT-4’s performance in extracting critical information from these judgments 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 that hold significance for both legal specialists and the general public, namely, the facts of the case, the claims made, references to legal statutes, references to precedents, general case outcome and corresponding labels, detailed order and remedies and, finally, reasons for the decision. The second task is more focused, aimed at analysing three of those extracted features, i.e., facts, claims and outcomes, in order to facilitate the development of a tool capable of predicting the outcome of employment law disputes. Through our analysis, we demonstrate that LLMs like GPT-4 can obtain high accuracy in legal information extraction, highlighting the potential of LLMs in revolutionising the way legal information is processed and utilised, offering significant implications for legal research and practice.