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.

翻译：近期研究表明，BERT、GPT等基础模型在适应各类下游任务方面表现卓越。这种适应性使其成为构建人工智能系统的主导力量。随着可视化技术与这些模型的交叉融合，一种新的研究范式应运而生。本文将这种交叉分为两个主要方向：面向基础模型的可视化（VIS4FM）与面向可视化的基础模型（FM4VIS）。在VIS4FM方向，我们探讨可视化在理解、优化和评估这些复杂模型中的核心作用，以应对透明度、可解释性、公平性和鲁棒性等迫切需求。而在FM4VIS方向，我们重点关注如何利用基础模型推动可视化领域自身的发展。基础模型与可视化的融合前景广阔，但同时也伴随着独特的挑战。通过阐明这些挑战与不断涌现的机遇，本文旨在为这一富有前景的研究方向提供持续探索的起点。