# Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling

arXiv: https://arxiv.org/abs/2402.17019 · HTML: https://mlsanity.com/paper/2402.17019/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 90 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [What Makes Reading Comprehension Questions Easier?](https://mlsanity.com/paper/1808.09384/) (2018)
- [The Next Chapter: A Study of Large Language Models in Storytelling](https://mlsanity.com/paper/2301.09790/) (2023)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Explaining Legal Concepts with Augmented Large Language Models (GPT-4)](https://mlsanity.com/paper/2306.09525/) (2023)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)
- [Plain English Summarization of Contracts](https://mlsanity.com/paper/1906.00424/) (2019)
- [Automatic Story Generation: Challenges and Attempts](https://mlsanity.com/paper/2102.12634/) (2021)
- [Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification](https://mlsanity.com/paper/2010.10035/) (2020)
- [Question Generation for Adaptive Education](https://mlsanity.com/paper/2106.04262/) (2021)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [Question-type Driven Question Generation](https://mlsanity.com/paper/1909.00140/) (2019)
- [QURIOUS: Question Generation Pretraining for Text Generation](https://mlsanity.com/paper/2004.11026/) (2020)
- [Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation](https://mlsanity.com/paper/2205.01730/) (2022)
- [ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://mlsanity.com/paper/2205.06982/) (2022)
- [The Law and NLP: Bridging Disciplinary Disconnects](https://mlsanity.com/paper/2310.14346/) (2023)
- [I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection](https://mlsanity.com/paper/2110.04123/) (2021)
- [Let Me Know What to Ask: Interrogative-Word-Aware Question Generation](https://mlsanity.com/paper/1910.13794/) (2019)
- [On the Automatic Generation and Simplification of Children's Stories](https://mlsanity.com/paper/2310.18502/) (2023)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)

## Built on by (most cited first, 23 of 23)

- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [M-IFEval: Multilingual Instruction-Following Evaluation](https://mlsanity.com/paper/2502.04688/) (2025)
- [Social Skill Training with Large Language Models](https://mlsanity.com/paper/2404.04204/) (2024)
- [Agents on the Bench: Large Language Model Based Multi Agent Framework for Trustworthy Digital Justice](https://mlsanity.com/paper/2412.18697/) (2024)
- [Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learning](https://mlsanity.com/paper/2502.12477/) (2025)
- [OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2503.16326/) (2025)
- [Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture](https://mlsanity.com/paper/2409.01556/) (2024)
- [When Style Breaks Safety: Defending LLMs Against Superficial Style Alignment](https://mlsanity.com/paper/2506.07452/) (2025)
- [CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation](https://mlsanity.com/paper/2508.05534/) (2025)
- [Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation](https://mlsanity.com/paper/2501.01743/) (2025)
- [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026)
- [SS-GEN: A Social Story Generation Framework with Large Language Models](https://mlsanity.com/paper/2406.15695/) (2024)
- [Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling](https://mlsanity.com/paper/2410.17210/) (2024)
- [Long Story Generation via Knowledge Graph and Literary Theory](https://mlsanity.com/paper/2508.03137/) (2025)
- [Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses](https://mlsanity.com/paper/2508.05009/) (2025)
- [Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making](https://mlsanity.com/paper/2509.19643/) (2025)
- [From Answers to Guidance: A Proactive Dialogue System for Legal Documents](https://mlsanity.com/paper/2510.19723/) (2025)
- [Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law](https://mlsanity.com/paper/2601.05879/) (2026)
- [CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation](https://mlsanity.com/paper/2602.12639/) (2026)
- [Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts](https://mlsanity.com/paper/2412.19966/) (2024)
- [Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India](https://mlsanity.com/paper/2507.06090/) (2025)
- [Evaluating Cultural Knowledge Processing in Large Language Models: A Cognitive Benchmarking Framework Integrating Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.01649/) (2025)

## Abstract (arXiv)

Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LLMs) in legal education to help non-experts learn intricate legal concepts through storytelling, an effective pedagogical tool in conveying complex and abstract concepts. We also introduce a new dataset LegalStories, which consists of 294 complex legal doctrines, each accompanied by a story and a set of multiple-choice questions generated by LLMs. To construct the dataset, we experiment with various LLMs to generate legal stories explaining these concepts. Furthermore, we use an expert-in-the-loop approach to iteratively design multiple-choice questions. Then, we evaluate the effectiveness of storytelling with LLMs through randomized controlled trials (RCTs) with legal novices on 10 samples from the dataset. We find that LLM-generated stories enhance comprehension of legal concepts and interest in law among non-native speakers compared to only definitions. Moreover, stories consistently help participants relate legal concepts to their lives. Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment. Our work has strong implications for using LLMs in promoting teaching and learning in the legal field and beyond.