Tech Note – using LLMs for structured info extraction

Using large language models for structured information extraction from the Innocence Project New Orleans’ wrongful conviction case files.

Exoneration documents, secured during legal proceedings that aim to right the wrongs of justice, are invaluable for understanding wrongful convictions. They cast a spotlight on law enforcement actions, revealing systemic challenges. Yet, finding and leveraging usable information within these collections remains a formidable task for researchers and advocates, due to their volume and unstructured heterogeneity.

This post introduces the methodology of the Innocence Discovery Lab, a collaboration between Innocence Project New Orleans (IPNO) and the Human Rights Data Analysis Group (HRDAG). Our approach utilizes data processing technologies, machine learning, and cross-referencing with both IPNO’s internal intake database and the Louisiana Law Enforcement Accountability Database (LLEAD). By developing a comprehensive index for these documents, targeting specific files, and analyzing cross-referenced data, we aim to unearth patterns and systemic issues that underpin wrongful convictions.

Read the full tech note here: Using large language models for structured information extraction from the Innocence Project New Orleans’ wrongful conviction case files.

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