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Data visualization creators often lack formal training, resulting in a knowledge gap in design practice.
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On the opportunities and risks of foundation models
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Toward a Unified Framework for Visualization Design Guidelines. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA ’21) . Association for Computing Machinery, New York, NY, USA, Article 240, 7 pages
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When machine learning meets privacy: A survey and outlook
Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. 2021a · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021b · 2021
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StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, 5356–5371
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Understanding Data Visualization Design Practice
P. Parsons. 2022 · 2021
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Understanding how Designers Find and Use Data Visualization Examples
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Ahmed Tlili, Boulus Shehata, Michael Agyemang Adarkwah, Aras Bozkurt, Daniel T. Hickey, Ronghuai Huang, and Brighter Agyemang. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Data Formulator: AI-powered Concept-driven Visualization Authoring
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Is ChatGPT a Good NLG Evaluator? A Preliminary Study
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Mathcoder: Seamless code integration in llms for enhanced mathematical reasoning
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Hannah K. Bako, Xinyi Liu, Leilani Battle, and Zhicheng Liu. 2023 · 2022
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“I learned it on the job” Becoming a Data Visualization professional in news media
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How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
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Training language models to follow instructions with human feedback
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Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22) . Association for Computing Machinery, New York, NY, USA, Article 332, 7 pages
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Self-consistency improves chain of thought reasoning in language models
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022a · 2022
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Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2023b · 2023
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A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
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VisEval: A Benchmark for Data Visualization in the Era of Large Language Models
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Enhancing Data Literacy On-demand: LLMs as Guides for Novices in Chart Interpretation
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Ask Humans or AI? Exploring Their Roles in Visualization Troubleshooting
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CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
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Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation
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Chartgpt: Leveraging llms to generate charts from abstract natural language
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DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24) . Association for Computing Machinery, New York, NY, USA, Article 985, 17 pages
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DracoGPT: Extracting Visualization Design Preferences from Large Language Models
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