Foundation Models Meet Visualizations: Challenges and Opportunities

• URL: http://arxiv.org/abs/2310.05771v1
• Date: Mon, 9 Oct 2023 14:57:05 GMT
• Title: Foundation Models Meet Visualizations: Challenges and Opportunities
• Authors: Weikai Yang, Mengchen Liu, Zheng Wang, and Shixia Liu
• Abstract summary: This paper divides visualizations for foundation models (VIS4FM) and foundation models for visualizations (FM4VIS) In VIS4FM, we explore the primary role of visualizations in understanding, refining, and evaluating these intricate models. In FM4VIS, we highlight how foundation models can be utilized to advance the visualization field itself.
• Score: 23.01218856618978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies have indicated that foundation models, such as BERT and GPT, excel in adapting to a variety of downstream tasks. This adaptability has established them as the dominant force in building artificial intelligence (AI) systems. As visualization techniques intersect with these models, a new research paradigm emerges. This paper divides these intersections into two main areas: visualizations for foundation models (VIS4FM) and foundation models for visualizations (FM4VIS). In VIS4FM, we explore the primary role of visualizations in understanding, refining, and evaluating these intricate models. This addresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, within FM4VIS, we highlight how foundation models can be utilized to advance the visualization field itself. The confluence of foundation models and visualizations holds great promise, but it also comes with its own set of challenges. By highlighting these challenges and the growing opportunities, this paper seeks to provide a starting point for continued exploration in this promising avenue.

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