用大模型自动识别表格列并生成可理解的转换规则。
TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations
- 根据列类型分类，用大模型生成对应转换逻辑。
- 在多领域真实数据上准确率更高，支持更多转换类型。
- 输出可读代码或公式，适合需要透明性的用户。
来自不同来源的表格数据整合常因格式和表示不一致而受阻，给数据分析人员和个人数字助手带来挑战。现有自动化表格转换方法范围有限，通常只针对特定转换类型或缺乏可解释性。本文提出TabulaX，一种利用大语言模型（LLM）实现多类列级表格转换的新框架。TabulaX首先将输入列分为四类：基于字符串、数值型、算法型和通用型，然后应用定制化方法生成人类可理解的转换函数，如数值公式或编程代码。该方法提升透明度，使用户能理解并修改映射关系。在多个真实世界数据集上的实验表明，TabulaX在准确率上优于现有最先进方法，支持更广泛的转换类别，并能高效生成可解释的转换结果。
原文摘要 · Abstract (English)
The integration of tabular data from diverse sources is often hindered by inconsistencies in formatting and representation, posing significant challenges for data analysts and personal digital assistants. Existing methods for automating tabular data transformations are limited in scope, often focusing on specific types of transformations or lacking interpretability. In this paper, we introduce TabulaX, a novel framework that leverages Large Language Models (LLMs) for multi-class column-level tabular transformations. TabulaX first classifies input columns into four transformation types (string-based, numerical, algorithmic, and general) and then applies tailored methods to generate human-interpretable transformation functions, such as numeric formulas or programming code. This approach enhances transparency and allows users to understand and modify the mappings. Through extensive experiments on real-world datasets from various domains, we demonstrate that TabulaX outperforms existing state-of-the-art approaches in terms of accuracy, supports a broader class of transformations, and generates interpretable transformations that can be efficiently applied.
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