AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Leong, C. W.; Jawahar, N.; Basheerabad, V.; Wörtwein, T.; Emerson, A.; Sivan, G.
Combining Generative and Discriminative AI for High-Stakes Interview Practice Proceedings Article
In: ACM Int. Conf. Proc. Ser., pp. 94–96, Association for Computing Machinery, 2024, ISBN: 979-840070463-5 (ISBN).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.