  Generative language models (LMs) are increasingly used for document
class-prediction tasks and promise enormous improvements in cost and
efficiency. Existing research often examines simple classification tasks, but
the capability of LMs to classify on complex or specialized tasks is less well
understood. We consider a highly complex task that is challenging even for
humans: the classification of legal reasoning according to jurisprudential
philosophy. Using a novel dataset of historical United States Supreme Court
opinions annotated by a team of domain experts, we systematically test the
performance of a variety of LMs. We find that generative models perform poorly
when given instructions (i.e. prompts) equal to the instructions presented to
human annotators through our codebook. Our strongest results derive from
fine-tuning models on the annotated dataset; the best performing model is an
in-domain model, LEGAL-BERT. We apply predictions from this fine-tuned model to
study historical trends in jurisprudence, an exercise that both aligns with
prominent qualitative historical accounts and points to areas of possible
refinement in those accounts. Our findings generally sound a note of caution in
the use of generative LMs on complex tasks without fine-tuning and point to the
continued relevance of human annotation-intensive classification methods.
