Enhancing data exploration through a pragmatic voice assistant

Abstract

Recent advancements in Natural Language Interfaces (NLIs), driven by powerful natural language models such as BERT, LLAMA, GPT, and ChatGPT, have generated considerable interest in enhancing human-computer interaction. However, a significant gap persists in the ability of these systems to facilitate genuinely natural conversations. Current NLIs often rely on users to initiate dialogue with wake words like "Alexa" or "Hey Siri," leading to interactions that lack the spontaneity and fluidity of human exchanges. This limitation is particularly problematic when considering the role of individual personality traits in communication. According to the attraction paradigm, users tend to prefer interaction styles that align with their personalities, suggesting that NLIs could benefit from being more attuned to users' unique conversational preferences. To address these challenges, this dissertation explores the concept of always-listening capabilities as a foundation for developing proactive AI systems. Drawing on literature from Pragmatics, the Psychology of spontaneous thought, and Personality research, I examine the subtleties of human communication, including contextual awareness, interruptions, the capacity for spontaneous engagement, and the relationship between characteristic personality traits and desired proactive behavior. This research highlights the potential for NLIs to achieve natural human interactions and engage users in deeper, more meaningful dialogues. In response to this need, I developed Articulate+, an NLI designed to investigate always-listening functionality, which eliminates the requirement for wake words and fosters ongoing conversations that reflect natural human interaction. Building on the insights gained from this exploration, I subsequently created ArticulatePro, a more advanced system focused on studying proactive behavior in voice assistants. ArticulatePro continuously listens to user interactions and proactively generates visualizations to enhance data exploration tasks. Through user studies comparing interactions with ArticulatePro and its non-proactive counterpart, I found that participants demonstrated higher levels of engagement and generated more data insights. The proactive nature of the system not only enhanced the reliability of user insights but also improved the system’s learnability and efficiency. Additionally, users exhibited a greater utilization of diverse chart types, leading to richer data analysis experiences. This research not only contributes to the design and functionality of NLIs but also deepens our understanding of how artificial intelligence can emulate human-like interaction, ultimately redefining user engagement with technology.