Zobrazeno 1 - 10
of 113 584
pro vyhledávání: '"Daly AN"'
Autor:
Padhi, Inkit, Nagireddy, Manish, Cornacchia, Giandomenico, Chaudhury, Subhajit, Pedapati, Tejaswini, Dognin, Pierre, Murugesan, Keerthiram, Miehling, Erik, Cooper, Martín Santillán, Fraser, Kieran, Zizzo, Giulio, Hameed, Muhammad Zaid, Purcell, Mark, Desmond, Michael, Pan, Qian, Vejsbjerg, Inge, Daly, Elizabeth M., Hind, Michael, Geyer, Werner, Rawat, Ambrish, Varshney, Kush R., Sattigeri, Prasanna
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM). These models offer comprehensive cover
Externí odkaz:
http://arxiv.org/abs/2412.07724
Autor:
Daly, Elizabeth M., Rooney, Sean, Tirupathi, Seshu, Garces-Erice, Luis, Vejsbjerg, Inge, Bagehorn, Frank, Salwala, Dhaval, Giblin, Christopher, Wolf-Bauwens, Mira L., Giurgiu, Ioana, Hind, Michael, Urbanetz, Peter
Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational d
Externí odkaz:
http://arxiv.org/abs/2412.01957
This paper presents a high-performance, scalable network monitoring and intrusion detection system (IDS) implemented in P4. The proposed solution is designed for high-performance environments such as cloud data centers, where ultra-low latency, high
Externí odkaz:
http://arxiv.org/abs/2411.17987
Autor:
Miehling, Erik, Desmond, Michael, Ramamurthy, Karthikeyan Natesan, Daly, Elizabeth M., Dognin, Pierre, Rios, Jesus, Bouneffouf, Djallel, Liu, Miao
Building pluralistic AI requires designing models that are able to be shaped to represent a wide range of value systems and cultures. Achieving this requires first being able to evaluate the degree to which a given model is capable of reflecting vari
Externí odkaz:
http://arxiv.org/abs/2411.12405
Autor:
Daly, Charles
In this paper we provide a means of certifying infinitesimal projective rigidity relative to the cusp for hyperbolic once punctured torus bundles in terms of twisted Alexander polynomials of representations associated to the holonomy. We also relate
Externí odkaz:
http://arxiv.org/abs/2411.04431
Large language models (LLMs) offer powerful capabilities but also introduce significant risks. One way to mitigate these risks is through comprehensive pre-deployment evaluations using benchmarks designed to test for specific vulnerabilities. However
Externí odkaz:
http://arxiv.org/abs/2410.12974
Autor:
Wagner, Nico, Desmond, Michael, Nair, Rahul, Ashktorab, Zahra, Daly, Elizabeth M., Pan, Qian, Cooper, Martín Santillán, Johnson, James M., Geyer, Werner
LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has be
Externí odkaz:
http://arxiv.org/abs/2410.11594
Autor:
Hussain, Sadam, Ali, Mansoor, Naseem, Usman, Palomo, Beatriz Alejandra Bosques, Molina, Mario Alexis Monsivais, Abdala, Jorge Alberto Garza, Avalos, Daly Betzabeth Avendano, Cardona-Huerta, Servando, Gulliver, T. Aaron, Pena, Jose Gerardo Tamez
Rising breast cancer (BC) occurrence and mortality are major global concerns for women. Deep learning (DL) has demonstrated superior diagnostic performance in BC classification compared to human expert readers. However, the predominant use of unimoda
Externí odkaz:
http://arxiv.org/abs/2410.10146
Autor:
Daly, Katharine, Eichner, Hubert, Kairouz, Peter, McMahan, H. Brendan, Ramage, Daniel, Xu, Zheng
Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millio
Externí odkaz:
http://arxiv.org/abs/2410.08892
Autor:
Ashktorab, Zahra, Desmond, Michael, Pan, Qian, Johnson, James M., Cooper, Martin Santillan, Daly, Elizabeth M., Nair, Rahul, Pedapati, Tejaswini, Achintalwar, Swapnaja, Geyer, Werner
Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as eval
Externí odkaz:
http://arxiv.org/abs/2410.00873