Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Roller, Stephen A."'
Autor:
Zhang, Jianguo, Roller, Stephen, Qian, Kun, Liu, Zhiwei, Meng, Rui, Heinecke, Shelby, Wang, Huan, Savarese, Silvio, Xiong, Caiming
End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models. This work enables the TOD systems with
Externí odkaz:
http://arxiv.org/abs/2308.08169
We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality of a machine-generated utterance, we leverage signals like user respons
Externí odkaz:
http://arxiv.org/abs/2307.14117
Autor:
Molybog, Igor, Albert, Peter, Chen, Moya, DeVito, Zachary, Esiobu, David, Goyal, Naman, Koura, Punit Singh, Narang, Sharan, Poulton, Andrew, Silva, Ruan, Tang, Binh, Liskovich, Diana, Xu, Puxin, Zhang, Yuchen, Kambadur, Melanie, Roller, Stephen, Zhang, Susan
We present a theory for the previously unexplained divergent behavior noticed in the training of large language models. We argue that the phenomenon is an artifact of the dominant optimization algorithm used for training, called Adam. We observe that
Externí odkaz:
http://arxiv.org/abs/2304.09871
Autor:
Aghajanyan, Armen, Yu, Lili, Conneau, Alexis, Hsu, Wei-Ning, Hambardzumyan, Karen, Zhang, Susan, Roller, Stephen, Goyal, Naman, Levy, Omer, Zettlemoyer, Luke
Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens from VQ-VAEs, speech tokens from HuBERT, BPE tokens for language or code
Externí odkaz:
http://arxiv.org/abs/2301.03728
Autor:
Shuster, Kurt, Xu, Jing, Komeili, Mojtaba, Ju, Da, Smith, Eric Michael, Roller, Stephen, Ung, Megan, Chen, Moya, Arora, Kushal, Lane, Joshua, Behrooz, Morteza, Ngan, William, Poff, Spencer, Goyal, Naman, Szlam, Arthur, Boureau, Y-Lan, Kambadur, Melanie, Weston, Jason
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks. We release both the model weights and co
Externí odkaz:
http://arxiv.org/abs/2208.03188
Autor:
Zhang, Susan, Roller, Stephen, Goyal, Naman, Artetxe, Mikel, Chen, Moya, Chen, Shuohui, Dewan, Christopher, Diab, Mona, Li, Xian, Lin, Xi Victoria, Mihaylov, Todor, Ott, Myle, Shleifer, Sam, Shuster, Kurt, Simig, Daniel, Koura, Punit Singh, Sridhar, Anjali, Wang, Tianlu, Zettlemoyer, Luke
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant
Externí odkaz:
http://arxiv.org/abs/2205.01068
Autor:
Shuster, Kurt, Komeili, Mojtaba, Adolphs, Leonard, Roller, Stephen, Szlam, Arthur, Weston, Jason
Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021). We extend the recent approach of Adolphs et al. (2021) to include inte
Externí odkaz:
http://arxiv.org/abs/2203.13224
Autor:
Smith, Eric Michael, Hsu, Orion, Qian, Rebecca, Roller, Stephen, Boureau, Y-Lan, Weston, Jason
At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016, arXiv:1603.08023), with human evaluations still considered the gold standard. Unfortunat
Externí odkaz:
http://arxiv.org/abs/2201.04723
We demonstrate that large language models are able to simulate Task Oriented Dialogues in novel domains, provided only with an API implementation and a list of goals. We show these simulations can formulate online, automatic metrics that correlate we
Externí odkaz:
http://arxiv.org/abs/2110.06905
Autor:
Doosthosseini, Mahsa, Aroom, Kevin R., Aroom, Majid, Culligan, Melissa, Naselsky, Warren, Thamire, Chandrasekhar, Haslach, Jr., Henry W., Roller, Stephen A., Hughen, James Richard, Friedberg, Joseph S., Hahn, Jin-Oh, Fathy, Hosam K.
This paper presents a novel mechatronic setup intended for providing respiratory support to patients suffering from pulmonary failure. The setup relies upon the circulation of an oxygenated perfluorocarbon (PFC) through the abdominal cavity. Such cir
Externí odkaz:
http://arxiv.org/abs/2107.02902