Zobrazeno 1 - 10
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pro vyhledávání: '"Peters, P E"'
Autor:
Groeneveld, Dirk, Beltagy, Iz, Walsh, Pete, Bhagia, Akshita, Kinney, Rodney, Tafjord, Oyvind, Jha, Ananya Harsh, Ivison, Hamish, Magnusson, Ian, Wang, Yizhong, Arora, Shane, Atkinson, David, Authur, Russell, Chandu, Khyathi Raghavi, Cohan, Arman, Dumas, Jennifer, Elazar, Yanai, Gu, Yuling, Hessel, Jack, Khot, Tushar, Merrill, William, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Pyatkin, Valentina, Ravichander, Abhilasha, Schwenk, Dustin, Shah, Saurabh, Smith, Will, Strubell, Emma, Subramani, Nishant, Wortsman, Mitchell, Dasigi, Pradeep, Lambert, Nathan, Richardson, Kyle, Zettlemoyer, Luke, Dodge, Jesse, Lo, Kyle, Soldaini, Luca, Smith, Noah A., Hajishirzi, Hannaneh
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important det
Externí odkaz:
http://arxiv.org/abs/2402.00838
Autor:
Soldaini, Luca, Kinney, Rodney, Bhagia, Akshita, Schwenk, Dustin, Atkinson, David, Authur, Russell, Bogin, Ben, Chandu, Khyathi, Dumas, Jennifer, Elazar, Yanai, Hofmann, Valentin, Jha, Ananya Harsh, Kumar, Sachin, Lucy, Li, Lyu, Xinxi, Lambert, Nathan, Magnusson, Ian, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Ravichander, Abhilasha, Richardson, Kyle, Shen, Zejiang, Strubell, Emma, Subramani, Nishant, Tafjord, Oyvind, Walsh, Pete, Zettlemoyer, Luke, Smith, Noah A., Hajishirzi, Hannaneh, Beltagy, Iz, Groeneveld, Dirk, Dodge, Jesse, Lo, Kyle
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to
Externí odkaz:
http://arxiv.org/abs/2402.00159
Autor:
Watt-Meyer, Oliver, Dresdner, Gideon, McGibbon, Jeremy, Clark, Spencer K., Henn, Brian, Duncan, James, Brenowitz, Noah D., Kashinath, Karthik, Pritchard, Michael S., Bonev, Boris, Peters, Matthew E., Bretherton, Christopher S.
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existin
Externí odkaz:
http://arxiv.org/abs/2310.02074
Autor:
Peng, Hao, Cao, Qingqing, Dodge, Jesse, Peters, Matthew E., Fernandez, Jared, Sherborne, Tom, Lo, Kyle, Skjonsberg, Sam, Strubell, Emma, Plessas, Darrell, Beltagy, Iz, Walsh, Evan Pete, Smith, Noah A., Hajishirzi, Hannaneh
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental concerns. Yet, progress on model efficiency has been impeded by practica
Externí odkaz:
http://arxiv.org/abs/2307.09701
The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model fro
Externí odkaz:
http://arxiv.org/abs/2305.15387
Autor:
Mahabadi, Rabeeh Karimi, Ivison, Hamish, Tae, Jaesung, Henderson, James, Beltagy, Iz, Peters, Matthew E., Cohan, Arman
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models to natural language remains challenging due to its discrete nature and the
Externí odkaz:
http://arxiv.org/abs/2305.08379
Pretrained language models (PLMs) are trained on massive corpora, but often need to specialize to specific domains. A parameter-efficient adaptation method suggests training an adapter for each domain on the task of language modeling. This leads to g
Externí odkaz:
http://arxiv.org/abs/2302.07027
Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been investigated for their interpretability and utility to end-users. However,
Externí odkaz:
http://arxiv.org/abs/2210.13575
This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our me
Externí odkaz:
http://arxiv.org/abs/2205.11961
Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We hypothesize that the information needed to steer the mo
Externí odkaz:
http://arxiv.org/abs/2205.05124