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

arXiv: https://arxiv.org/abs/2410.03762 · HTML: https://mlsanity.com/paper/2410.03762/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Can GPT-3 Perform Statutory Reasoning?](https://mlsanity.com/paper/2302.06100/) (2023)
- [Explaining Legal Concepts with Augmented Large Language Models (GPT-4)](https://mlsanity.com/paper/2306.09525/) (2023)
- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)
- [How well do SOTA legal reasoning models support abductive reasoning?](https://mlsanity.com/paper/2304.06912/) (2023)
- [JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice](https://mlsanity.com/paper/2308.02032/) (2023)
- [Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process](https://mlsanity.com/paper/2311.13281/) (2023)

## Built on by (most cited first, 6 of 6)

- [Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice](https://mlsanity.com/paper/2412.15260/) (2024)
- [That's So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral](https://mlsanity.com/paper/2509.07170/) (2025)
- [LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agents](https://mlsanity.com/paper/2512.04105/) (2025)
- [Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law](https://mlsanity.com/paper/2601.05879/) (2026)
- [Retrieving Semantically Similar Decisions under Noisy Institutional Labels: Robust Comparison of Embedding Methods](https://mlsanity.com/paper/2512.05681/) (2025)
- [In With the Old: Enhancing 'Classical' Document Automation with Generative AI](https://mlsanity.com/paper/2610.07480/) (2026)

## Abstract (arXiv)

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.