Automatic Information Extraction From Employment Tribunal Judgements
  Using Large Language Models
Joana Ribeiro de Faria Huiyuan Xie Felix Steffek
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 that hold
significance for both legal specialists and the general public, including the
facts of the case, the claims made, references to legal statutes, references to
precedents, general case outcomes and corresponding labels, detailed order and
remedies and reasons for the decision. The second task is more focused, aimed
at analysing three of those extracted features, namely 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.