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pro vyhledávání: '"Bhambhoria, Rohan"'
This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific kno
Externí odkaz:
http://arxiv.org/abs/2404.12349
Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such as law, experts often require interpretability for automatic systems
Externí odkaz:
http://arxiv.org/abs/2305.16490
In recent years, large language models (LLMs) have achieved strong performance on benchmark tasks, especially in zero or few-shot settings. However, these benchmarks often do not adequately address the challenges posed in the real-world, such as that
Externí odkaz:
http://arxiv.org/abs/2305.15282
Hate speech causes widespread and deep-seated societal issues. Proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex t
Externí odkaz:
http://arxiv.org/abs/2305.13677
Parameter-efficient tuning aims to mitigate the large memory requirements of adapting pretrained language models for downstream tasks. For example, one popular method, prefix-tuning, prepends trainable tokens to sequences while freezing the rest of t
Externí odkaz:
http://arxiv.org/abs/2305.12086
Seeking legal advice is often expensive. Recent advancements in machine learning for solving complex problems can be leveraged to help make legal services more accessible to the public. However, real-life applications encounter significant challenges
Externí odkaz:
http://arxiv.org/abs/2210.13712
Over the past several years, legal applications of deep learning have been on the rise. However, as with other high-stakes decision making areas, the requirement for interpretability is of crucial importance. Current models utilized by legal practiti
Externí odkaz:
http://arxiv.org/abs/2201.01164
Akademický článek
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Autor:
Dolatabadi E; Faculty of Health, School of Health Policy and Management, York University, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada.; Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada., Moyano D; Vector Institute, Toronto, ON, Canada., Bales M; Hoffmann-La Roche Ltd, Toronto, ON, Canada., Spasojevic S; Hoffmann-La Roche Ltd, Toronto, ON, Canada., Bhambhoria R; Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada., Bhatti J; Manulife, Toronto, ON, Canada., Debnath S; Deloitte, Toronto, ON, Canada., Hoell N; Deloitte, Toronto, ON, Canada., Li X; Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada., Leng C; Hoffmann-La Roche Ltd, Toronto, ON, Canada., Nanda S; Deloitte, Toronto, ON, Canada., Saab J; TELUS Health, Montreal, QC, Canada., Sahak E; Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada., Sie F; Hoffmann-La Roche Ltd, Toronto, ON, Canada., Uppal S; TELUS Health, Montreal, QC, Canada., Vadlamudi NK; Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada., Vladimirova A; Roche Information Solutions, San Francisco, CA, United States., Yakimovich A; Hoffmann-La Roche Ltd, Munich, Germany., Yang X; Deloitte, Toronto, ON, Canada., Kocak SA; Vector Institute, Toronto, ON, Canada., Cheung AM; Department of Medicine and Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.; University Health Network, Toronto, ON, Canada.
Publikováno v:
Journal of medical Internet research [J Med Internet Res] 2023 Dec 08; Vol. 25, pp. e55010. Date of Electronic Publication: 2023 Dec 08.