Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs

2026-01-22

AI Summary

Traditional data preparation methods face limitations in semantic understanding and generalization, struggling to meet the rapidly growing demand for application-ready data. This work systematically reviews the application of large language models (LLMs) in three core tasks—data cleaning, integration, and augmentation—and proposes a task-centered taxonomy that, for the first time, delineates the evolutionary trajectory of LLM-driven data preparation techniques. Through a comprehensive literature review, the study examines key technologies such as prompt engineering, agent-based architectures, and semantic matching, alongside prevailing datasets and evaluation metrics. It highlights LLMs’ strengths in enhancing generalization and semantic comprehension while identifying critical challenges related to computational cost, hallucination, scalability, and the lack of standardized evaluation frameworks. The paper concludes by outlining a roadmap for future research and development in this emerging field.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM-enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data preparation. By investigating hundreds of recent literature works, this paper presents a systematic review of this evolving landscape, focusing on the use of LLM techniques to prepare data for diverse downstream tasks. First, we characterize the fundamental paradigm shift, from rule-based, model-specific pipelines to prompt-driven, context-aware, and agentic preparation workflows. Next, we introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning (e.g., standardization, error processing, imputation), data integration (e.g., entity matching, schema matching), and data enrichment (e.g., data annotation, profiling). For each task, we survey representative techniques, and highlight their respective strengths (e.g., improved generalization, semantic understanding) and limitations (e.g., the prohibitive cost of scaling LLMs, persistent hallucinations even in advanced agents, the mismatch between advanced methods and weak evaluation). Moreover, we analyze commonly used datasets and evaluation metrics (the empirical part). Finally, we discuss open research challenges and outline a forward-looking roadmap that emphasizes scalable LLM-data systems, principled designs for reliable agentic workflows, and robust evaluation protocols.

Problem

• data preparation
• large language models
• data cleaning
• data integration
• data enrichment

Innovation

• LLM-enhanced data preparation
• agentic workflows
• task-centric taxonomy
• data cleaning
• semantic understanding