Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models

TL;DR

This paper explores using large language models to streamline legal intake processes, combining logical rules and LLMs to improve eligibility assessment efficiency and accuracy in civil legal services.

Contribution

It introduces a digital intake platform integrating LLMs with logical rules and evaluates their effectiveness in legal eligibility screening.

Findings

Best model achieved an F1 score of 0.82

Approach reduces time and resources needed for legal intake

Minimizes false negatives in eligibility assessment

Abstract

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 eligibility criteria are nuanced, open-textured, and require frequent revision as grants start and end. In this paper, we investigate the use of large language models (LLMs) to reduce this burden. We describe a digital intake platform that combines logical rules with LLMs to offer eligibility recommendations, and we evaluate the ability of 8 different LLMs to perform this task. We find promising results for this approach to help close the access to justice gap, with the best model reaching an F1 score of .82, while minimizing false negatives.