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pro vyhledávání: '"Chen, Jennifer"'
Given the black-box nature and complexity of large transformer language models (LM), concerns about generalizability and robustness present ethical implications for domains such as hate speech (HS) detection. Using the content rich Social Bias Frames
Externí odkaz:
http://arxiv.org/abs/2411.06213
Autor:
Radhakrishnan, Prashanth, Chen, Jennifer, Xu, Bo, Ramaswami, Prem, Pho, Hannah, Olmos, Adriana, Manyika, James, Guha, R. V.
Large Language Models (LLMs) are prone to generating factually incorrect information when responding to queries that involve numerical and statistical data or other timely facts. In this paper, we present an approach for enhancing the accuracy of LLM
Externí odkaz:
http://arxiv.org/abs/2409.13741
Autor:
Tseng, Tiffany, Davidson, Matt J., Morales-Navarro, Luis, Chen, Jennifer King, Delaney, Victoria, Leibowitz, Mark, Beason, Jazbo, Shapiro, R. Benjamin
Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands
Externí odkaz:
http://arxiv.org/abs/2311.09088
Autor:
Guha, Ramanathan V., Radhakrishnan, Prashanth, Xu, Bo, Sun, Wei, Au, Carolyn, Tirumali, Ajai, Amjad, Muhammad J., Piekos, Samantha, Diaz, Natalie, Chen, Jennifer, Wu, Julia, Ramaswami, Prem, Manyika, James
Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different
Externí odkaz:
http://arxiv.org/abs/2309.13054
Autor:
Tseng, Tiffany, Chen, Jennifer King, Abdelrahman, Mona, Kery, Mary Beth, Hohman, Fred, Hilliard, Adriana, Shapiro, R. Benjamin
Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering al
Externí odkaz:
http://arxiv.org/abs/2304.05444
Autor:
Dietz, Griffin, Chen, Jennifer King, Beason, Jazbo, Tarrow, Matthew, Hilliard, Adriana, Shapiro, R. Benjamin
Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in
Externí odkaz:
http://arxiv.org/abs/2207.08974
Autor:
Naaseh, Ariana *, Roshal, Joshua, Silvestri, Caitlin, Woodward, John M., Thornton, Steven W., L'Huillier, Joseph C., Hunt, Maya, Sathe, Tejas S., Hoagland, Darian L., Godley, Frederick, IV, Jindani, Rajika, Tieken, Kelsey R, Rodriguez, Jorge G. Zárate *, Anand, Ananya, Chen, Jennifer H., Navarro, Sergio M., Lund, Sarah
Publikováno v:
In Journal of Surgical Education October 2024 81(10):1394-1399
