Break, Repair, Learn, Break Less: Investigating User Preferences for Assignment of Divergent Phrasing Learning Burden in Human-Agent Interaction to Minimize Conversational Breakdowns

Conversational agents (CA) occasionally fail to understand the user's intention or respond inappropriately due to natural language complexity. These conversational breakdowns can happen because of low intent and entity prediction confidence scores. A promising repair strategy in such cases is that the CA proposes to users likely alternatives...

Harnessing Large Language Models for Cognitive Assistants in Factories

As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities,...