TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations

2024-11-26

arXiv.org

AI Summary

Heterogeneous tabular data pose significant integration challenges due to inconsistencies in format and semantics. Method: This paper proposes the first large language model (LLM)-based framework for multi-category table transformation. It introduces a novel automatic classification mechanism for four transformation types—string, numeric, algorithmic, and generic—and leverages table semantic understanding, rule-guided prompt engineering, multi-stage task decomposition, and function synthesis to generate human-readable, editable, function-level transformations (e.g., Excel formulas or Python code). Contribution/Results: Evaluated on real-world datasets across multiple domains, the framework achieves statistically significant accuracy improvements over state-of-the-art methods, supports a broader spectrum of transformation types, and ensures full human interpretability of all outputs—thereby balancing automation capability with transparency and explainability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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 tabular transformations. TabulaX first classifies input tables into four transformation classes (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.

Problem

• Automating multi-class column-level tabular transformations
• Addressing inconsistencies in tabular data formatting and representation
• Generating human-interpretable transformation functions for transparency

Innovation

• Leverages LLMs for multi-class table transformations
• Classifies columns into four transformation types
• Generates human-interpretable transformation functions