Zero-shot Language Modeling
Zero-Shot Language Modeling is a language modeling paradigm in which a model generates predictions or performs tasks in the absence of explicit training examples for those tasks, relying solely on its pre-trained knowledge and implicit understanding of task descriptions.
219
Publications
18.3K
Citations
1K
Authors
184
Institutions
Publications per year
2017–2026
216
Parents
219
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre et al. · arXiv (Cornell University) · 2022 · 1.2K citations · Full text
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Gu, Machel Reid et al. · arXiv (Cornell University) · 2022 · 1.1K citations · Full text
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Peng Gao, Shijie Geng, Renrui Zhang et al. · International Journal of Computer Vision · 2023 · 919 citations
1–5 of 219
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Korea Advanced Institute of Science and Technology, South Korea
5 publications · 110 citations
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5
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184
University of Washington
Seattle, United States
8 h-index
25 publications
4.9K citations
Meta (Israel)
Tel Aviv, Israel
8 h-index
36 publications
4.9K citations
University of California, Berkeley
Berkeley, United States
8 h-index
18 publications
2.4K citations
Tsinghua University
Beijing, China
8 h-index
67 publications
3.5K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
7 h-index
18 publications
608 citations
Stanford University
Stanford, United States
7 h-index
18 publications
2.7K citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
7 h-index
11 publications
432 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
22 publications
1.1K citations
1–8 of 184
Repository · 87 publications · 8.3K citations
33
h-index
Conference · 7 publications · 247 citations
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Conference · 6 publications · 192 citations
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Repository · 3 publications · 56 citations
3
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Conference · 3 publications · 73 citations
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