Large Language Models for Data Visualization
暫譯: 用於資料視覺化的大型語言模型

Hoque Prince, Enamul

Large Language Models for Data Visualization

商品描述

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.

商品描述(中文翻譯)

本書提供第一本全面理解與應用大型語言模型（large language models, LLMs）於資料視覺化的指南。隨著 LLM 持續重新定義人們與資料互動的方式，本書探討這些模型如何讓自然語言成為視覺化、分析與推理的強大介面。本書結合現代語言模型的基礎，以及正在快速發展、並重新形塑視覺化建立、探索、溝通與無障礙化方式的研究成果。

本書從 transformer、multimodal LLM、prompt engineering 與 agentic AI 背後的原理開始，逐步建立讀者理解當今 AI 系統所需的知識，接著探討它們在整個視覺化流程中的應用。內容涵蓋視覺化理解與互動、圖表問答（chart question answering）、對話式與多模態介面、視覺化生成與編輯、自動化洞察與故事生成、人類與 LLM 協作創作，以及 AI 驅動的無障礙化與共融式視覺化。

除了應用之外，本書也檢視 LLM 驅動之視覺化系統的能力、限制與可信度，並探討幻覺（hallucinations）、推理錯誤、評估、負責任的 AI、人機協作、個人化、多語言視覺化，以及未來的研究機會等主題。本書以豐富的真實世界範例、基準測試與案例研究為內容基礎，既是易於理解的入門讀物，也是權威性的參考資料，適合希望打造新一代智慧、可信賴且以人為本之視覺化系統的研究人員、研究生、教育工作者與實務工作者。

作者簡介

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.

作者簡介(中文翻譯)

Enamul Hoque Prince 是加拿大 York University 的副教授，並領導 Intelligent Visualization Lab。此前，他曾任 Stanford University 博士後研究員，並取得 University of British Columbia 的 Computer Science 博士學位。他的研究結合了 Natural Language Processing 與 Information Visualization——這兩個過去各自獨立發展的領域——開創以語言為基礎的資料互動與推理新方向。他透過多模態 AI 與代理式系統推進這項研究，使資料分析更加易於使用且值得信賴。

他的研究成果包括廣泛採用的基準資料集與模型，協助形塑多模態 AI 應用於資料視覺化的新興領域，並獲得包括 OpenAI、Google 與 Anthropic 在內的領先 AI 組織採用。Prince 博士已在頂尖的 AI、視覺化與 HCI 相關期刊及研討會發表近 100 篇論文，獲得多項研究獎項，並在國際會議中發表受邀演講及教學課程。他曾擔任 ACL Rolling Review 的領域主席，以及 IEEE VIS 程式委員會委員。他的研究獲得 NSERC、Canada Foundation for Innovation、National Research Council Canada 及產業合作夥伴的支持。

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