LegalViz：基于文本转图表生成的法律文本可视化
LegalViz: Legal Text Visualization by Text To Diagram Generation
AI总结：
针对非专家理解法律文件需专业知识的问题，提出LegalViz数据集（23种语言、7010对法律文件与可视化案例），用DOT语言生成图表可视化法律文本，还提出新评估指标，实证显示其训练模型优于GPTs等现有模型。
摘要：
包含判决书和法院命令在内的法律文件需要高度复杂的法律知识才能理解。为了向非专家披露专家知识，我们探索了用易于理解的图表可视化法律文本的问题，并提出了一个新的LegalViz数据集，该数据集包含23种语言、7010个法律文件与可视化对案例，使用Graphviz的DOT图描述语言构建。LegalViz能从复杂的法律语料库中生成简洁图表，可一目了然地识别每个判决中至关重要的法律实体、交易、法律渊源和陈述。此外，我们通过考虑图结构、文本相似性和法律内容，为法律图表可视化提供了新的评估指标。我们对少样本和微调大型语言模型生成法律图表进行了实证研究，并使用这些指标（包括23种语言内基于法律内容的评估）进行了评估。用LegalViz训练的模型优于包括GPTs在内的现有模型，证实了我们数据集的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
