  Visual analytics supports data analysis tasks within complex domain problems.
However, due to the richness of data types, visual designs, and interaction
designs, users need to recall and process a significant amount of information
when they visually analyze data. These challenges emphasize the need for more
intelligent visual analytics methods. Large language models have demonstrated
the ability to interpret various forms of textual data, offering the potential
to facilitate intelligent support for visual analytics. We propose LEVA, a
framework that uses large language models to enhance users' VA workflows at
multiple stages: onboarding, exploration, and summarization. To support
onboarding, we use large language models to interpret visualization designs and
view relationships based on system specifications. For exploration, we use
large language models to recommend insights based on the analysis of system
status and data to facilitate mixed-initiative exploration. For summarization,
we present a selective reporting strategy to retrace analysis history through a
stream visualization and generate insight reports with the help of large
language models. We demonstrate how LEVA can be integrated into existing visual
analytics systems. Two usage scenarios and a user study suggest that LEVA
effectively aids users in conducting visual analytics.
