Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Xiong, Betty"'
Autor:
Bommasani, Rishi, Klyman, Kevin, Kapoor, Sayash, Longpre, Shayne, Xiong, Betty, Maslej, Nestor, Liang, Percy
Foundation models are increasingly consequential yet extremely opaque. To characterize the status quo, the Foundation Model Transparency Index was launched in October 2023 to measure the transparency of leading foundation model developers. The Octobe
Externí odkaz:
http://arxiv.org/abs/2407.12929
Autor:
Bolton, Elliot, Xiong, Betty, Muralidharan, Vijaytha, Schamroth, Joel, Muralidharan, Vivek, Manning, Christopher D., Daneshjou, Roxana
Large language models, such as GPT-4 and Med-PaLM, have shown impressive performance on clinical tasks; however, they require access to compute, are closed-source, and cannot be deployed on device. Mid-size models such as BioGPT-large, BioMedLM, LLaM
Externí odkaz:
http://arxiv.org/abs/2404.15894
Autor:
Bolton, Elliot, Venigalla, Abhinav, Yasunaga, Michihiro, Hall, David, Xiong, Betty, Lee, Tony, Daneshjou, Roxana, Frankle, Jonathan, Liang, Percy, Carbin, Michael, Manning, Christopher D.
Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameters, are computationally expensive to run, require users to send their i
Externí odkaz:
http://arxiv.org/abs/2403.18421
Autor:
Bommasani, Rishi, Klyman, Kevin, Longpre, Shayne, Xiong, Betty, Kapoor, Sayash, Maslej, Nestor, Narayanan, Arvind, Liang, Percy
Publikováno v:
Published in AIES 2024
Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose F
Externí odkaz:
http://arxiv.org/abs/2402.16268
Autor:
Bommasani, Rishi, Klyman, Kevin, Longpre, Shayne, Kapoor, Sayash, Maslej, Nestor, Xiong, Betty, Zhang, Daniel, Liang, Percy
Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation models is growing, transparency is on the decline, mirroring t
Externí odkaz:
http://arxiv.org/abs/2310.12941
Associating biological context with protein-protein interactions through text mining at PubMed scale
Autor:
Sosa, Daniel N., Hintzen, Rogier, Xiong, Betty, de Giorgio, Alex, Fauqueur, Julien, Davies, Mark, Lever, Jake, Altman, Russ B.
Publikováno v:
In Journal of Biomedical Informatics September 2023 145
Publikováno v:
In JAAD International March 2024 14:29-30
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Xiong B; Department of Biomedical Data Science, Stanford University, Stanford, California., Zou J; Department of Biomedical Data Science, Stanford University, Stanford, California., Ali W; UCB Pharma, Slough, UK., Daneshjou R; Department of Biomedical Data Science and Dermatology, Stanford University, Stanford, California., Williams J; UCB Pharma, Brussels, Belgium.
Publikováno v:
JAAD international [JAAD Int] 2023 Oct 21; Vol. 14, pp. 29-30. Date of Electronic Publication: 2023 Oct 21 (Print Publication: 2024).