Multitask Prompted Training Enables Zero-Shot Task Generalization

Autor: Sanh, Victor, Webson, Albert, Raffel, Colin, Bach, Stephen, Sutawika, Lintang, Alyafeai, Zaid, Chaffin, Antoine, Stiegler, Arnaud, Le Scao, Teven, Raja, Arun, Dey, Manan, Bari, M Saiful, Xu, Canwen, Thakker, Urmish, Sharma, Shanya, Szczechla, Eliza, Kim, Taewoon, Chhablani, Gunjan, V. Nayak, Nihal, Datta, Debajyoti, Chang, Jonathan, Jiang, Mike, Wang, Han, Manica, Matteo, Shen, Sheng, Yong, Zheng-Xin, Pandey, Harshit, Mckenna, Michael, Bawden, Rachel, Wang, Thomas, Neeraj, Trishala, Rozen, Jos, Sharma, Abheesht, Santilli, Andrea, Fevry, Thibault, Fries, Jason, Teehan, Ryan, Bers, Tali, Biderman, Stella, Gao, Leo, Wolf, Thomas, Rush, Alexander

Publikováno v: ICLR 2022-Tenth International Conference on Learning Representations
ICLR 2022-Tenth International Conference on Learning Representations, Apr 2022, Online, Unknown Region

International audience; Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d03ed16ffb7aca406595ddc481bad1fd
https://inria.hal.science/hal-03540072