2026-04-13T01:43:03Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4115432026-01-27T03:42:54Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Vázquez Alcocer, Pere Pau author 2024 © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from "creativity" to "precision", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. Visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works in the literature have utilized them for the creation of visualizations. And some tools such as Lida, incorporate them as part of their pipeline. Nevertheless, to the authors’ knowledge, no systematic approach for testing their capabilities has been published, which includes both extensive and in-depth evaluation. Our goal is to fill that gap with a systematic approach that analyzes three elements: whether Large Language Models are capable of correctly generating a large variety of charts, what libraries they can deal with effectively, and how far we can go to configure individual charts. To achieve this objective, we initially selected a diverse set of charts, which are commonly utilized in data visualization. We then developed a set of generic prompts that could be used to generate them, and analyzed the performance of different LLMs and libraries. The results include both the set of prompts and the data sources, as well as an analysis of the performance with different configurations. Supported by Ministerio de Ciencia e Innovación/AEI (PID2021-122136OB-C21 by 10.13039/501100011033/FEDER, UE). Peer Reviewed Postprint (author's final draft) Àrees temàtiques de la UPC::Informàtica::Infografia Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural Information visualization Visualization techniques Empirical studies in visualization Industries Technological innovation Systematics Soft sensors Pipelines Data visualization Programming Bing Chat ChatGPT-4 Generative models Visualització de la informació Are LLMs ready for visualization?