Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

28 March 2023

Papers citing "Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning"

7 / 107 papers shown

Title
Multitask Prompted Training Enables Zero-Shot Task Generalization Victor Sanh Albert Webson Colin Raffel Stephen H. Bach Lintang Sutawika ... T. Bers Stella Biderman Leo Gao Thomas Wolf Alexander M. Rush LRM 211 1,656 0 15 Oct 2021
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer Tu Vu Brian Lester Noah Constant Rami Al-Rfou Daniel Matthew Cer VLM LRM 137 277 0 15 Oct 2021
Exploring Universal Intrinsic Task Subspace via Prompt Tuning Yujia Qin Xiaozhi Wang Yusheng Su Yankai Lin Ning Ding ... Juanzi Li Lei Hou Peng Li Maosong Sun Jie Zhou VLM VPVLM 103 26 0 15 Oct 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,843 0 18 Apr 2021
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters Aston Zhang Yi Tay Shuai Zhang Alvin Chan A. Luu S. Hui Jie Fu MQ 166 83 0 17 Feb 2021
WARP: Word-level Adversarial ReProgramming Karen Hambardzumyan Hrant Khachatrian Jonathan May AAML 254 342 0 01 Jan 2021
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 243 1,817 0 17 Sep 2019