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pro vyhledávání: '"Poth, Clifton"'
Autor:
Engländer, Leon, Sterz, Hannah, Poth, Clifton, Pfeiffer, Jonas, Kuznetsov, Ilia, Gurevych, Iryna
Generalization and robustness to input variation are core desiderata of machine learning research. Language varies along several axes, most importantly, language instance (e.g. French) and domain (e.g. news). While adapting NLP models to new language
Externí odkaz:
http://arxiv.org/abs/2407.01091
Autor:
Poth, Clifton, Sterz, Hannah, Paul, Indraneil, Purkayastha, Sukannya, Engländer, Leon, Imhof, Timo, Vulić, Ivan, Ruder, Sebastian, Gurevych, Iryna, Pfeiffer, Jonas
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible config
Externí odkaz:
http://arxiv.org/abs/2311.11077
Autor:
Struppek, Lukas, Hentschel, Martin B., Poth, Clifton, Hintersdorf, Dominik, Kersting, Kristian
Backdoor attacks pose a serious security threat for training neural networks as they surreptitiously introduce hidden functionalities into a model. Such backdoors remain silent during inference on clean inputs, evading detection due to inconspicuous
Externí odkaz:
http://arxiv.org/abs/2310.06372
Autor:
Baumgärtner, Tim, Wang, Kexin, Sachdeva, Rachneet, Eichler, Max, Geigle, Gregor, Poth, Clifton, Sterz, Hannah, Puerto, Haritz, Ribeiro, Leonardo F. R., Pfeiffer, Jonas, Reimers, Nils, Şahin, Gözde Gül, Gurevych, Iryna
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., generative, discriminative), and
Externí odkaz:
http://arxiv.org/abs/2203.13693
Intermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language models, it has become infeasible to run the cross-product of all combinat
Externí odkaz:
http://arxiv.org/abs/2104.08247
Autor:
Pfeiffer, Jonas, Rücklé, Andreas, Poth, Clifton, Kamath, Aishwarya, Vulić, Ivan, Ruder, Sebastian, Cho, Kyunghyun, Gurevych, Iryna
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained models is expensive, slow, and time-consuming, which impedes progress t
Externí odkaz:
http://arxiv.org/abs/2007.07779