71 citations · 342 across the 13 of their papers we have counts for
4 papers · 1 filter
On Transferability of Prompt Tuning for Natural Language Processing
Yusheng Su, Xiaozhi Wang, Yujia Qin +10
Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-paramet…
Exploring Universal Intrinsic Task Subspace via Prompt Tuning
Yujia Qin, Xiaozhi Wang, Yusheng Su +10
Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find…
Knowledge Inheritance for Pre-trained Language Models
Yujia Qin, Yankai Lin, Jing Yi +8
Recent explorations of large-scale pre-trained language models (PLMs) have revealed the power of PLMs with huge amounts of parameters, setting off a wave of training ever-larger PL…
CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models
Yusheng Su, Xu Han, Yankai Lin +5
Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently. However, in many low-resource scenarios, the conventiona…