Zobrazeno 1 - 10
of 489
pro vyhledávání: '"Ju, Da"'
People tend to use language to mention surprising properties of events: for example, when a banana is blue, we are more likely to mention color than when it is yellow. This fact is taken to suggest that yellowness is somehow a typical feature of bana
Externí odkaz:
http://arxiv.org/abs/2408.02948
Autor:
Xu, Jing, Ju, Da, Lane, Joshua, Komeili, Mojtaba, Smith, Eric Michael, Ung, Megan, Behrooz, Morteza, Ngan, William, Moritz, Rashel, Sukhbaatar, Sainbayar, Boureau, Y-Lan, Weston, Jason, Shuster, Kurt
We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety. We are publicly rel
Externí odkaz:
http://arxiv.org/abs/2306.04707
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
The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve. Unfortunately, such exchanges in the wild will not always involve human utterances that are benign or of
Externí odkaz:
http://arxiv.org/abs/2208.03295
Attention mechanisms have become a standard tool for sequence modeling tasks, in particular by stacking self-attention layers over the entire input sequence as in the Transformer architecture. In this work we introduce a novel attention procedure cal
Externí odkaz:
http://arxiv.org/abs/2106.04279
Autor:
Goyal, Naman, Gao, Cynthia, Chaudhary, Vishrav, Chen, Peng-Jen, Wenzek, Guillaume, Ju, Da, Krishnan, Sanjana, Ranzato, Marc'Aurelio, Guzman, Francisco, Fan, Angela
One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restrict
Externí odkaz:
http://arxiv.org/abs/2106.03193
Autor:
Sukhbaatar, Sainbayar, Ju, Da, Poff, Spencer, Roller, Stephen, Szlam, Arthur, Weston, Jason, Fan, Angela
Attention mechanisms have shown promising results in sequence modeling tasks that require long-term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories. However, not all content in the past
Externí odkaz:
http://arxiv.org/abs/2105.06548
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases. We investigate a variety of methods to mitigate these issues in
Externí odkaz:
http://arxiv.org/abs/2010.07079
Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller
Externí odkaz:
http://arxiv.org/abs/2010.01082
Autor:
Roller, Stephen, Boureau, Y-Lan, Weston, Jason, Bordes, Antoine, Dinan, Emily, Fan, Angela, Gunning, David, Ju, Da, Li, Margaret, Poff, Spencer, Ringshia, Pratik, Shuster, Kurt, Smith, Eric Michael, Szlam, Arthur, Urbanek, Jack, Williamson, Mary
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet. We present a b
Externí odkaz:
http://arxiv.org/abs/2006.12442