Large Language Models for Data Visualization (Synthesis Lectures on Visualization)

This book offers the first comprehensive guide to understanding and applying large language models (LLMs) in data visualization. As LLMs continue to redefine how people interact with data, the book explores how they have made natural language a powerful interface for visualization, analysis, and reasoning. This book brings together the foundations of modern language models with the rapidly evolving research that is reshaping how visualizations are created, explored, communicated, and made accessible. 
Beginning with the principles behind transformers, multimodal LLMs, prompt engineering, and agentic AI, the book builds the knowledge needed to understand today’s AI systems before exploring their applications throughout the visualization pipeline. It covers visualization understanding and interaction, chart question answering, conversational and multimodal interfaces, visualization generation and editing, automated insight and story generation, human–LLM collaborative authoring, and AI-powered accessibility and inclusive visualization.
Beyond applications, the book examines the capabilities, limitations, and trustworthiness of LLM-powered visualization systems, addressing topics such as hallucinations, reasoning errors, evaluation, responsible AI, human–AI collaboration, personalization, multilingual visualization, and future research opportunities. Richly illustrated with real-world examples, benchmarks, and case studies, this book serves as both an accessible introduction and an authoritative reference for researchers, graduate students, educators, and practitioners seeking to build the next generation of intelligent, trustworthy, and human-centered visualization systems.

Enamul Hoque Prince is an Associate Professor at York University, Canada, where he directs the Intelligent Visualization Lab. Previously, he was a postdoctoral fellow at Stanford University and earned his Ph.D. in Computer Science from the University of British Columbia. His research unites Natural Language Processing and Information Visualization―two fields that have historically evolved separately―to establish a new frontier in language-based data interaction and reasoning. His work advances this frontier through multimodal AI and agentic systems, making data analytics more accessible and trustworthy. His research has produced widely adopted benchmarks and models that have helped shape the emerging field of multimodal AI for data visualization and have been adopted by leading AI organizations, including OpenAI, Google, and Anthropic. Dr. Prince has published nearly 100 papers in leading AI, visualization, and HCI venues, received multiple research awards, and delivered invited talks and tutorials at international conferences. He has served as an Area Chair for the ACL Rolling Review and on the IEEE VIS Program Committee. His research has been supported by NSERC, the Canada Foundation for Innovation, the National Research Council Canada, and industry partners.