3 citations · 3 across the 5 of their papers we have counts for
5 papers
On Wednesdays, We Ask Questions: Optimizing "Active Listening" in Automated Legal Triage and Referral
Quinten Steenhuis, Jacqueline Harvey
The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. In this paper, we describe an…
Forgetting as a Feature: Cognitive Alignment of Large Language Models
Alexandros Christoforos
Large Language Models (LLMs) are often evaluated against ideals of perfect Bayesian inference, yet growing evidence suggests that their in-context reasoning exhibits systematic for…
That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral
Quinten Steenhuis
Each year millions of people seek help for their legal problems by calling a legal aid program hotline, walking into a legal aid office, or using a lawyer referral service. The fir…
Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models
Quinten Steenhuis, Hannes Westermann
Legal intake, the process of finding out if an applicant is eligible for help from a free legal aid program, takes significant time and resources. In part this is because eligibili…
Weaving Pathways for Justice with GPT: LLM-driven automated drafting of interactive legal applications
Quinten Steenhuis, David Colarusso, Bryce Willey
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…