Web: http://arxiv.org/abs/2108.13161

Jan. 28, 2022, 2:10 a.m. | Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen

cs.CL updates on arXiv.org arxiv.org

Large-scale pre-trained language models have contributed significantly to
natural language processing by demonstrating remarkable abilities as few-shot
learners. However, their effectiveness depends mainly on scaling the model
parameters and prompt design, hindering their implementation in most real-world
applications. This study proposes a novel pluggable, extensible, and efficient
approach named DifferentiAble pRompT (DART), which can convert small language
models into better few-shot learners without any prompt engineering. The main
principle behind this approach involves reformulating potential natural
language processing tasks into …

arxiv language language models models

More from arxiv.org / cs.CL updates on arXiv.org

Data Scientist

@ Fluent, LLC | Boca Raton, Florida, United States

Big Data ETL Engineer

@ Binance.US | Vancouver

Data Scientist / Data Engineer

@ Kin + Carta | Chicago

Data Engineer

@ Craft | Warsaw, Masovian Voivodeship, Poland

Senior Manager, Data Analytics Audit

@ Affirm | Remote US

Data Scientist - Nationwide Opportunities, AWS Professional Services

@ Amazon.com | US, NC, Virtual Location - N Carolina