Helping Language Models Learn More: Multi-Dimensional Task Prompt for Few-shot Tuning-Reference-Cited by-同舟云学术

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Helping Language Models Learn More: Multi-Dimensional Task Prompt for Few-shot Tuning

Published:2023-10-01 Issue: Volume:33 Page:746-752
ISSN:
Container-title:2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Weng Jinta¹,Zhang Jiarui¹,Hu Yue¹,Fa Daidong²,Xu Xiaofeng³,Huang Heyan⁴

Affiliation:

1. School of Cyber Security, University of Chinese Academy of Sciences,China

2. School of Computer and Information, Qiannan Normal University for Nationalities,Duyun,China

3. School of Computer Science and Cyber Engineering, Guangzhou University,Guangzhou,China

4. School of Computer Science and Technology, Southeast Academy of Information Technology, Beijing Institute of Technology,China

Funder

National Natural Science Foundation of China

Chinese Academy of Science

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394280.pdf?arnumber=10394280

Reference23 articles.

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Devlin;arXiv e-prints,2018

2. Language models are few-shot learners;Brown;Advances in neural information processing systems,2020

3. How Can We Know What Language Models Know?

4. The Power of Scale for Parameter-Efficient Prompt Tuning

5. Are Prompt-based Models Clueless?

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