Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization-Reference-Cited by-同舟云学术

Subspace Selection based Prompt Tuning with Nonconvex Nonsmooth Black-Box Optimization

Published:2024-08-24 Issue: Volume:24 Page:4179-4190
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Haozhen¹^ORCID,Zhang Hualin²^ORCID,Gu Bin³^ORCID,Chang Yi⁴^ORCID

Affiliation:

1. School of Artificial Intelligence, Jilin University, Changchun, Jilin, China

2. Mohamed bin Zayed University of Artificial Intelligence, Masdar, United Arab Emirates

3. School of Artificial Intelligence, Jilin University & Mohamed bin Zayed University of Artificial Intelligence, Changchun, Jilin, China

4. School of Artificial Intelligence, Jilin University & Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, Changchun, Jilin, China

Funder

National Key R&D Program of China under Grant

National Natural Science Foundation of China through grants

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671986

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5. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.