Combining Generative and Discriminative AI for High-Stakes Interview Practice
Chee Wee Leong, Navaneeth Jawahar, Vinay Basheerabad, Torsten Wörtwein, Andrew Emerson, Guy Sivan · 2024
We present a demo comprising an end-to-end AI pipeline for practicing video interviews for a high-stakes scenarios (i.e., college admissions) with personalized, actionable feedback for continuous improvement of the user. This system provides personalized, actionable feedback for continuous user improvement. Utilizing large language models (LLMs), we generate questions and responses for a virtual avatar interviewer. Our focus on key qualities—such as concise responses with low latency, empathy, and smooth topic navigation—led to a comparative evaluation of several prominent LLMs, each undergoing evolutionary development. We also discuss the integration of avatar technology to create an immersive, virtual environment for naturalistic dyadic conversations.