Systems that support users in the automatic creation of visualizations must address several subtasks - understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. In this work, we pose visualization generation as a multi-stage generation problem and argue that well-orchestrated pipelines based on large language models (LLMs) such as ChatGPT/GPT-4 and image generation models (IGMs) are suitable to addressing these tasks. We present LIDA, a novel tool for generating grammar-agnostic visualizations and infographics. LIDA comprises of 4 modules - A SUMMARIZER that converts data into a rich but compact natural language summary, a GOAL EXPLORER that enumerates visualization goals given the data, a VISGENERATOR that generates, refines, executes and filters visualization code and an INFOGRAPHER module that yields data-faithful stylized graphics using IGMs. LIDA provides a python api, and a hybrid user interface (direct manipulation and multilingual natural language) for interactive chart, infographics and data story generation. Learn more about the project here - https://microsoft.github.io/lida/

翻译：支持用户自动创建可视化系统的工具必须解决多个子任务——理解数据语义、枚举相关可视化目标，以及生成可视化规范。本文将可视化生成视为一个多阶段生成问题，并论证基于大语言模型（如ChatGPT/GPT-4）和图像生成模型的精心编排流水线适合解决这些任务。我们提出LIDA这一新型工具，用于生成语法无关的可视化与信息图。LIDA包含四个模块：SUMMARIZER将数据转换为丰富而紧凑的自然语言摘要，GOAL EXPLORER根据数据枚举可视化目标，VISGENERATOR生成、优化、执行并筛选可视化代码，INFOGRAPHER模块则利用图像生成模型生成忠实于数据的风格化图形。LIDA提供Python API和混合用户界面（直接操作与多语言自然语言），用于交互式图表、信息图及数据故事的生成。了解更多项目信息请访问：https://microsoft.github.io/lida/