Agent-based interfaces for data analysis: bridging the gap between graphical and command-line interfaces

Music City Center 

Large Language Models (LLMs) present new opportunities for interactive
data analysis. LLM-based approaches may help resolve the tension
between accessibility and flexibility that challenges the design of
effective data analysis interfaces. Graphical user interfaces present
simple, intuitive controls at the expense of flexibility, while
programmatic interfaces support arbitrarily complex tasks, but only
for those with sufficient time and skill. By allowing the user to
express tasks in natural language, LLMs can interpolate between the
two extremes. We present a prototype of an agent-based system for
analyzing genomic data that is targeted at users with varying levels
of computational skill.