Tabular Data Wrangling in the Era of Large Language Model: A Survey

Data quality is a critical factor in data-centric artificial intelligence (AI), as “dirty data” can significantly hinder analysis and degrade downstream model performance. Among various data formats, tabular data is a ubiquitous and essential form of structured information, widely used across domains such as business, finance, healthcare, and scientific research. Tabular data wrangling, encompassing tasks, such as error correction, missing value imputation, data normalization, semantic interpretation, and the integration of heterogeneous sources, is a foundational step in the data preparation pipeline. While traditional statistical and deep learning approaches have shown competitive results, they often face limitations in robustness, manual effort, and generalization. Recent advances in large language models (LLMs), whose capabilities have expanded from unstructured text to structured data through techniques like prompt engineering and supervised fine-tuning, offer promising alternatives for addressing these challenges. Although prior surveys have examined LLM applications in downstream tabular tasks, such as question answering and fact verification, limited attention has been given to their potential in improving data quality. This survey aims to fill that gap by providing a comprehensive overview along four dimensions: 1) formal definitions and categorization of tabular wrangling tasks, 2) the typical workflow of applying LLMs to tabular data, 3) the roles LLMs can play in various wrangling methods, and 4) a comparative analysis of LLM-based versus traditional approaches, highlighting their respective strengths and limitations.