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Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Feb. 28, 2024, 5:49 a.m. | Hang Jiang, Xiajie Zhang, Robert Mahari, Daniel Kessler, Eric Ma, Tal August, Irene Li, Alex 'Sandy' Pentland, Yoon Kim, Jad Kabbara, Deb Roy
cs.CL updates on arXiv.org arxiv.org
Abstract: 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 …
abstract application arxiv concepts cs.cl cs.hc democracy documents experts general knowledge language language models large language large language models legal literacy making novel paper people storytelling through type
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