# Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process

arXiv: https://arxiv.org/abs/2311.13281 · HTML: https://mlsanity.com/paper/2311.13281/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Eliciting Human Preferences with Language Models](https://mlsanity.com/paper/2310.11589/) (2023)
- [Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations](https://mlsanity.com/paper/2311.05584/) (2023)
- [JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justice](https://mlsanity.com/paper/2308.02032/) (2023)

## Built on by (most cited first, 4 of 4)

- [LawLLM: Law Large Language Model for the US Legal System](https://mlsanity.com/paper/2407.21065/) (2024)
- [Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models](https://mlsanity.com/paper/2410.03762/) (2024)
- [It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human](https://mlsanity.com/paper/2407.06798/) (2024)
- [DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation](https://mlsanity.com/paper/2606.13931/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) and chatbots show significant promise in streamlining the legal intake process. This advancement can greatly reduce the workload and costs for legal aid organizations, improving availability while making legal assistance more accessible to a broader audience. However, a key challenge with current LLMs is their tendency to overconfidently deliver an immediate 'best guess' to a client's question based on the output distribution learned over the training data. This approach often overlooks the client's actual intentions or the specifics of their legal situation. As a result, clients may not realize the importance of providing essential additional context or expressing their underlying intentions, which are crucial for their legal cases. Traditionally, logic based decision trees have been used to automate intake for specific access to justice issues, such as immigration and eviction. But those solutions lack scalability. We demonstrate a proof-of-concept using LLMs to elicit and infer clients' underlying intentions and specific legal circumstances through free-form, language-based interactions. We also propose future research directions to use supervised fine-tuning or offline reinforcement learning to automatically incorporate intention and context elicitation in chatbots without explicit prompting.