Instruction Tuning
Instruction Tuning is a model-level optimization method that refines a pre-trained language model through supervised fine-tuning on collections of human-annotated instructions and their corresponding outputs to improve its ability to generalize across downstream tasks.
156
Publications
15K
Citations
1.1K
Authors
123
Institutions
Publications per year
2017–2026
156
Parents
156
Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu et al. · Nature · 2023 · 3.2K citations · Full text
Computing, Health Informatics, Computational Linguistics +16
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre et al. · arXiv (Cornell University) · 2022 · 1.2K citations · Full text
Improved Baselines with Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Yuheng Li et al. · 2024 · 1.2K citations
Haotian Liu, Chunyuan Li, Qingyang Wu et al. · arXiv (Cornell University) · 2023 · 681 citations · Full text
1–5 of 156
1.1K
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5
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4
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Korea Advanced Institute of Science and Technology, South Korea
4 publications · 88 citations
4
h-index
1–5 of 1.1K
123
Tencent (China)
Shenzhen, China
6 h-index
24 publications
954 citations
University of Washington
Seattle, United States
6 h-index
25 publications
5K citations
ShanghaiTech University
Shanghai, China
3 h-index
3 publications
876 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
3 publications
1.3K citations

Zhejiang University
Hangzhou, China
3 h-index
12 publications
434 citations
Columbia University
New York, United States
3 h-index
5 publications
874 citations
Shanghai Clinical Research Center
Shanghai, China
2 h-index
2 publications
849 citations
Stanford University
Stanford, United States
2 h-index
5 publications
1.5K citations
1–8 of 123
Repository · 95 publications · 6K citations
37
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Conference · 5 publications · 108 citations
5
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Repository · 2 publications · 108 citations
2
h-index
JAMIA · 2 publications · 71 citations
2
h-index
1 publications · 92 citations
1
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1–5