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pro vyhledávání: '"Cherif, Lynn"'
Autor:
Lee, Seanie, Kim, Minsu, Cherif, Lynn, Dobre, David, Lee, Juho, Hwang, Sung Ju, Kawaguchi, Kenji, Gidel, Gauthier, Bengio, Yoshua, Malkin, Nikolay, Jain, Moksh
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing effective protection against many modes of attack prompts requires disco
Externí odkaz:
http://arxiv.org/abs/2405.18540
Autor:
Cherif, Lynn, Safdar, Mutahar, Lamouche, Guy, Wanjara, Priti, Paul, Padma, Wood, Gentry, Zimmermann, Max, Hannesen, Florian, Zhao, Yaoyao Fiona
Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importanc
Externí odkaz:
http://arxiv.org/abs/2308.14861