Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Prakash, Chandana Satya"'
Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering. The retrieve-and-read approach is widely adopted for integrating external knowledge into a language model. However, this
Externí odkaz:
http://arxiv.org/abs/2406.04670
Autor:
Soltan, Saleh, Ananthakrishnan, Shankar, FitzGerald, Jack, Gupta, Rahul, Hamza, Wael, Khan, Haidar, Peris, Charith, Rawls, Stephen, Rosenbaum, Andy, Rumshisky, Anna, Prakash, Chandana Satya, Sridhar, Mukund, Triefenbach, Fabian, Verma, Apurv, Tur, Gokhan, Natarajan, Prem
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various
Externí odkaz:
http://arxiv.org/abs/2208.01448
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
http://arxiv.org/abs/2206.07808
Autor:
Li, Shuyang, Sridhar, Mukund, Prakash, Chandana Satya, Cao, Jin, Hamza, Wael, McAuley, Julian
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types. Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete label
Externí odkaz:
http://arxiv.org/abs/2204.13796
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3be35e4212a3f3d4bb494e7d071b86ca
https://doi.org/10.1145/3534678.3539173
https://doi.org/10.1145/3534678.3539173
