ArticulatePro: A Comparative Study on a Proactive and Non-Proactive Assistant in a Climate Data Exploration Task

TL;DR

This paper compares proactive and non-proactive voice assistants in climate data exploration, demonstrating that proactive assistants improve engagement and speed of insights through pragmatic NLP approaches.

Contribution

It introduces a proactive digital assistant leveraging pragmatics in NLP, and evaluates its effectiveness in a climate data exploration task.

Findings

Proactive assistant increased user engagement.

Proactive assistant enabled quicker insights.

Participants preferred proactive interactions.

Abstract

Recent advances in Natural Language Interfaces (NLIs) and Large Language Models (LLMs) have transformed our approach to NLP tasks, shifting the focus towards a more Pragmatics-based approach. This shift enables more natural interactions between humans and voice assistants, which have been historically difficult to achieve. Pragmatics involves understanding how users often talk 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 the proactive assistant enhanced user engagement and facilitated quicker insights. Our study highlights the potential of Pragmatic, proactive AI in NLIs and identifies key challenges in its implementation, offering insights for future research.