A Pragmatics-based Approach to Proactive Digital Assistants for Data Exploration

DOI

Recent advances in Natural Language Interfaces (NLIs) and Large Language Models (LLMs) have transformed the way we tackle NLP tasks, shifting the focus towards a more Pragmatics-based perspective. This shift enables more natural interactions between humans and voice assistants, which have historically been difficult to achieve. Pragmatics involves understanding how users often speak out of turn, interrupt one another, or provide relevant information without being explicitly asked (maxim of quantity). To explore this, we developed a digital assistant that continuously listens to conversations and proactively generates relevant visualizations during data exploration tasks. In a within-subject study, participants interacted with both proactive and non-proactive versions of a voice assistant while exploring the Hawaii Climate Data Portal (HCDP). Results suggest that interaction with the proactive assistant increased the total number of utterances and discoveries, facilitated quicker and more reliable insights, and led to greater usage of the system's chart capabilities. Our study highlights the potential of proactive AI in NLIs and identifies key challenges in its implementation, offering insights for future research.