Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications
This addresses the problem of speeding up legal tool creation for self-represented litigants, but it is incremental as it builds on existing AI and legal automation methods.
The paper tackled automating court form completion for self-represented litigants by testing three approaches using GPT models, concluding that a hybrid method with constrained automated drafting and human review is best suited for authoring guided interviews.
Can generative AI help us speed up the authoring of tools to help self-represented litigants? In this paper, we describe 3 approaches to automating the completion of court forms: a generative AI approach that uses GPT-3 to iteratively prompt the user to answer questions, a constrained template-driven approach that uses GPT-4-turbo to generate a draft of questions that are subject to human review, and a hybrid method. We use the open source Docassemble platform in all 3 experiments, together with a tool created at Suffolk University Law School called the Assembly Line Weaver. We conclude that the hybrid model of constrained automated drafting with human review is best suited to the task of authoring guided interviews.