Foundation model assisted visual analytics: Opportunities and Challenges

We explore the integration of foundation models, such as large language models (LLMs) and multimodal LLMs (MLLMs), into visual analytics (VA) systems through intuitive natural language interactions. We survey current research directions in this emerging field, examining how foundation models have already been integrated into key visualisation-related processes in VA: visual mapping, the creation of data visualisations; visualisation observation, the process of generating a finding through visualisation; and visualisation manipulation, changing the viewport or highlighting areas of interest within a visualisation. We also highlight new possibilities that foundation models bring to VA, in particular, the opportunities to use MLLMs to interpret visualisations directly, to integrate multimodal interactions, and to provide guidance to users. We finally conclude with a vision of future VA systems as collaborative partners in analysis and address the prominent challenges in realising this vision through foundation models. Our discussions in this paper aim to guide future researchers working on foundation model assisted VA systems and help them navigate common obstacles when developing these systems.