Tech Note – improving LLM-driven info extraction
Improving LLM-driven information extraction from the Innocence Project New Orleans’ wrongful conviction case files
Exoneration documents, documents acquired during the legal processes that seek to rectify miscarriages of justice, offer invaluable insights into wrongful conviction cases. Particularly, they illuminate the roles and actions of law enforcement personnel. Yet, the sheer volume and lack of structure in these documents pose challenges for researchers, lawyers, and advocates dedicated to transparency and justice.
In this follow-up chapter, we explore how recent advancements in large language model (“LLM”) technology, including expanded context windows and more cost-effective high-performing models, impact our extraction strategies. We reexamine our information extraction pipeline by evaluating the performance of proprietary models and open-source alternatives in extracting police officers’ names and roles from wrongful conviction case files.
Read the full tech note here: Improving LLM-driven information extraction from the Innocence Project New Orleans’ wrongful conviction case files