Classifying complex documents: comparing bespoke solutions to large
  language models
Glen Hopkins Kristjan Kalm
Here we search for the best automated classification approach for a set of
complex legal documents. Our classification task is not trivial: our aim is to
classify ca 30,000 public courthouse records from 12 states and 267 counties at
two different levels using nine sub-categories. Specifically, we investigated
whether a fine-tuned large language model (LLM) can achieve the accuracy of a
bespoke custom-trained model, and what is the amount of fine-tuning necessary.