Adaptive Prompts
Adaptive Prompts is a computational framework within natural language processing that dynamically tailors prompt structures or contents based on contextual inputs, user interactions, or model performance to optimize task accuracy and learning efficiency.
67
Publications
5.1K
Citations
284
Authors
92
Institutions
Publications per year
2017–2026
53
Parents
67
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
Renrui Zhang, Jiaming Han, Peng Gao et al. · arXiv (Cornell University) · 2023 · 171 citations · Full text
Enabling Conversational Interaction with Mobile UI using Large Language Models
Bryan Wang, Gang Li, Yang Li · 2023 · 141 citations · Full text
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2 publications · 131 citations
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University of Chinese Academy of Sciences
Beijing, China
5 h-index
12 publications
836 citations
University of Science and Technology of China
Hefei, China
3 h-index
7 publications
196 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
6 publications
6.1K citations

Chinese Academy of Sciences
Beijing, China
3 h-index
11 publications
1.1K citations

Zhejiang University
Hangzhou, China
2 h-index
8 publications
358 citations

University of Minnesota
Minneapolis, United States
2 h-index
6 publications
195 citations
Huawei Technologies (China)
Shenzhen, China
2 h-index
6 publications
185 citations

Peking University
Beijing, China
2 h-index
9 publications
375 citations
1–8 of 92
Repository · 14 publications · 617 citations
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3 publications · 410 citations
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1 publications · 32 citations
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Repository · 1 publications · 52 citations
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1 publications · 124 citations
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