Classifying complex documents: comparing bespoke solutions to large language models

Glen Hopkins, Kristjan Kalm
12/12/2023
cs.CLcs.LG

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Abstract

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.

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Cite this paper

@article{hopkins2023classifying,
  title  = {Classifying complex documents: comparing bespoke solutions to large language models},
  author = {Glen Hopkins and Kristjan Kalm},
  year   = {2023},
  eprint = {2312.07182},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.07182v1}
}

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