LegalViz: Legal Text Visualization by Text To Diagram Generation
This work addresses the challenge of making legal documents more accessible to non-experts through visualization, but it is incremental as it builds on existing text-to-diagram methods with a new dataset and evaluation metrics.
The authors tackled the problem of visualizing complex legal texts for non-experts by creating a dataset called LegalViz with 7,010 legal document and diagram pairs across 23 languages, and they showed that models trained on this dataset outperform existing models like GPTs in generating legal diagrams.
Legal documents including judgments and court orders require highly sophisticated legal knowledge for understanding. To disclose expert knowledge for non-experts, we explore the problem of visualizing legal texts with easy-to-understand diagrams and propose a novel dataset of LegalViz with 23 languages and 7,010 cases of legal document and visualization pairs, using the DOT graph description language of Graphviz. LegalViz provides a simple diagram from a complicated legal corpus identifying legal entities, transactions, legal sources, and statements at a glance, that are essential in each judgment. In addition, we provide new evaluation metrics for the legal diagram visualization by considering graph structures, textual similarities, and legal contents. We conducted empirical studies on few-shot and finetuning large language models for generating legal diagrams and evaluated them with these metrics, including legal content-based evaluation within 23 languages. Models trained with LegalViz outperform existing models including GPTs, confirming the effectiveness of our dataset.