Lawma: The Power of Specialization for Legal Annotation
This provides a viable alternative to prompting commercial models for empirical legal researchers, though it is incremental in improving annotation efficiency.
The paper tackles the problem of legal text annotation by introducing CaselawQA, a benchmark with 260 tasks, and shows that small fine-tuned models outperform commercial models like GPT-4.5 and Claude 3.7 Sonnet, achieving higher accuracy with only a few hundred to a thousand labeled examples.
Annotation and classification of legal text are central components of empirical legal research. Traditionally, these tasks are often delegated to trained research assistants. Motivated by the advances in language modeling, empirical legal scholars are increasingly turning to prompting commercial models, hoping that it will alleviate the significant cost of human annotation. Despite growing use, our understanding of how to best utilize large language models for legal annotation remains limited. To bridge this gap, we introduce CaselawQA, a benchmark comprising 260 legal annotation tasks, nearly all new to the machine learning community. We demonstrate that commercial models, such as GPT-4.5 and Claude 3.7 Sonnet, achieve non-trivial yet highly variable accuracy, generally falling short of the performance required for legal work. We then demonstrate that small, lightly fine-tuned models outperform commercial models. A few hundred to a thousand labeled examples are usually enough to achieve higher accuracy. Our work points to a viable alternative to the predominant practice of prompting commercial models. For concrete legal annotation tasks with some available labeled data, researchers are likely better off using a fine-tuned open-source model.