mlsanityData to 7 Oct 2026

LegalViz: Legal Text Visualization by Text To Diagram Generation

arXiv 2502.06147 ↗First posted 10 Feb 2025cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 54 references that are in the map. First posted on arXiv 10 Feb 2025.

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30 of 54 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FloCA: Towards Faithful and Logically Consistent Flowchart Reasoningcited by 0