Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Yeh, Catherine"'
Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential ha
Externí odkaz:
http://arxiv.org/abs/2410.01088
Autor:
Chen, Yida, Wu, Aoyu, DePodesta, Trevor, Yeh, Catherine, Li, Kenneth, Marin, Nicholas Castillo, Patel, Oam, Riecke, Jan, Raval, Shivam, Seow, Olivia, Wattenberg, Martin, Viégas, Fernanda
Conversational LLMs function as black box systems, leaving users guessing about why they see the output they do. This lack of transparency is potentially problematic, especially given concerns around bias and truthfulness. To address this issue, we p
Externí odkaz:
http://arxiv.org/abs/2406.07882
Large language models (LLMs) are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. However, LLM-powered writing systems can frustrate users due to their limited personalization and control, wh
Externí odkaz:
http://arxiv.org/abs/2402.08855
Transformer models are revolutionizing machine learning, but their inner workings remain mysterious. In this work, we present a new visualization technique designed to help researchers understand the self-attention mechanism in transformers that allo
Externí odkaz:
http://arxiv.org/abs/2305.03210
People read digital documents on a daily basis to share, exchange, and understand information in electronic settings. However, current document readers create a static, isolated reading experience, which does not support users' goals of gaining more
Externí odkaz:
http://arxiv.org/abs/2302.07492
Autor:
Saunders, William, Yeh, Catherine, Wu, Jeff, Bills, Steven, Ouyang, Long, Ward, Jonathan, Leike, Jan
We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our models help humans find flaws in summaries that they w
Externí odkaz:
http://arxiv.org/abs/2206.05802
Autor:
BUTLER, JENNA1, YEH, CATHERINE2
Publikováno v:
Communications of the ACM. Oct2022, Vol. 65 Issue 10, p34-41. 8p. 1 Color Photograph, 1 Chart.
Autor:
Alegria, Sharla (AUTHOR), Yeh, Catherine (AUTHOR)
Publikováno v:
Rotman Management. Spring2024, p117-120. 4p.