Classifying complex documents: comparing bespoke solutions to large language models

Glen Hopkins; Kristjan Kalm

Classifying complex documents: comparing bespoke solutions to large language models

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arxiv 2312.07182 v1 pith:YVLPDS6R submitted 2023-12-12 cs.CL cs.LG

Glen Hopkins , Kristjan Kalm This is my paper · ORCID

classification cs.CLcs.LG

keywords bespokeclassificationcomplexdocumentslanguagelargemodelaccuracy

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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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