自动为表格生成条件格式规则，无需用户手动设置。
Tabularis Formatus: Predictive Formatting for Tables
- 结合语言模型语义与符号推理，自动生成格式触发条件和样式。
- 在180万份公开表格数据上测试，准确率比现有系统高15.6%~26.5%。
- 适合不熟悉公式或界面的普通用户，提升数据可视化效率。
电子表格软件广泛用于表格数据管理与分析，但条件格式（CF）规则的创建仍需专业技术知识和平台经验。本文提出TaFo，一种神经符号方法，用于生成表格的CF建议，解决用户缺乏意识、规则创建困难及界面不足等问题。TaFo借鉴组件化合成系统，融入语言模型的语义知识与多样性保持的规则排序机制。不同于仅关注结构格式的方法，TaFo首次实现基于数值的格式化，自动学习规则触发条件与对应视觉样式。通过消除现有技术依赖的格式示例或自然语言指令，使格式化完全预测与自动化。我们使用包含180万份带条件格式的公开工作簿语料库进行评估，对比多种符号与神经模型。结果表明，TaFo生成的建议更准确、多样且完整，在匹配用户实际添加规则方面优于现有系统15.6%至26.5%。
原文摘要 · Abstract (English)
Spreadsheet manipulation software are widely used for data management and analysis of tabular data, yet the creation of conditional formatting (CF) rules remains a complex task requiring technical knowledge and experience with specific platforms. In this paper we present TaFo, a neuro-symbolic approach to generating CF suggestions for tables, addressing common challenges such as user unawareness, difficulty in rule creation, and inadequate user interfaces. TaFo takes inspiration from component based synthesis systems and extends them with semantic knowledge of language models and a diversity preserving rule ranking.Unlike previous methods focused on structural formatting, TaFo uniquely incorporates value-based formatting, automatically learning both the rule trigger and the associated visual formatting properties for CF rules. By removing the dependency on user specification used by existing techniques in the form of formatted examples or natural language instruction, TaFo makes formatting completely predictive and automated for the user. To evaluate TaFo, we use a corpus of 1.8 Million public workbooks with CF and manual formatting. We compare TaFo against a diverse set of symbolic and neural systems designed for or adapted for the task of table formatting. Our results show that TaFo generates more accurate, diverse and complete formatting suggestions than current systems and outperforms these by 15.6\%--26.5\% on matching user added ground truth rules in tables.
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