Publications

@inproceedings{leong_combining_2024,

title = {Combining Generative and Discriminative AI for High-Stakes Interview Practice},

author = {C. W. Leong and N. Jawahar and V. Basheerabad and T. Wörtwein and A. Emerson and G. Sivan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211135262&doi=10.1145%2f3686215.3688377&partnerID=40&md5=4f53f4466d43840510a36c125eeefa16},

doi = {10.1145/3686215.3688377},

isbn = {979-840070463-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {94–96},

publisher = {Association for Computing Machinery},

abstract = {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. © 2024 Owner/Author.},

keywords = {AI systems, College admissions, Continuous improvements, End to end, Interactive computer graphics, Interactive dialog system, interactive dialogue systems, Language Model, Modeling languages, Multi-modal, Multimodal computing, Video interview, video interviews, Virtual avatar, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{si_generative_2023,

title = {Generative AI Models for Virtual Interviewers: Applicability and Performance Comparison},

author = {J. Si and J. Song and M. Woo and D. Kim and Y. Lee and S. Kim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203492324&doi=10.1049%2ficp.2024.0193&partnerID=40&md5=84eb48f6b51c941da9c77fa3aba46262},

doi = {10.1049/icp.2024.0193},

isbn = {27324494 (ISSN)},

year  = {2023},

date = {2023-01-01},

booktitle = {IET. Conf. Proc.},

volume = {2023},

pages = {27–28},

publisher = {Institution of Engineering and Technology},

abstract = {Interviewing processes are considered crucial steps in job hunting or college admissions, and effective practice plays a significant role in successfully navigating these stages. Although various platforms have recently emerged for practicing virtual interviews, they often lack the tension and realism of actual interviews due to repetitive and formal content. This study aims to analyze and compare the performance of different generative AI models for creating a diverse set of virtual interviewers. Specifically, we examine the characteristics and applicability of each model, as well as the differences and advantages between them, and evaluate the performance of the generated virtual interviewers. Through this analysis, we aim to propose solutions for enhancing the practicality and efficiency of virtual interviews. © The Institution of Engineering & Technology 2023.},

keywords = {3D Generation, College admissions, Digital elevation model, Effective practices, Generative AI, Job hunting, Metaverse, Metaverses, Performance, Performance comparison, Virtual environments, Virtual Interview, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}