A pilot study finds GPT-4o extracts 73% of fields from photos of a lease form, with accuracy dropping from 98% on typed copies to 60% on low-quality handwritten photos.
Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications
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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.
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Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice
A pilot study finds GPT-4o extracts 73% of fields from photos of a lease form, with accuracy dropping from 98% on typed copies to 60% on low-quality handwritten photos.