# LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries

arXiv: https://arxiv.org/abs/2501.01711 · HTML: https://mlsanity.com/paper/2501.01711/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Place Matters: Comparing LLM Hallucination Rates for Place-Based Legal Queries](https://mlsanity.com/paper/2511.06700/) (2025)
- [Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models](https://mlsanity.com/paper/2601.05144/) (2026)
- [Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law](https://mlsanity.com/paper/2601.05879/) (2026)

## Abstract (arXiv)

The paper presents a preliminary analysis of an experiment conducted by Frank Bold, a Czech expert group, to explore user interactions with GPT-4 for addressing legal queries. Between May 3, 2023, and July 25, 2023, 1,252 users submitted 3,847 queries. Unlike studies that primarily focus on the accuracy, factuality, or hallucination tendencies of large language models (LLMs), our analysis focuses on the user query dimension of the interaction. Using GPT-4o for zero-shot classification, we categorized queries on (1) whether users provided factual information about their issue (29.95%) or not (70.05%), (2) whether they sought legal information (64.93%) or advice on the course of action (35.07%), and (3) whether they imposed requirements to shape or control the model's answer (28.57%) or not (71.43%). We provide both quantitative and qualitative insight into user needs and contribute to a better understanding of user engagement with LLMs.