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cs.CL2022★ 1 cited
On the Effectiveness of Parameter-Efficient Fine-Tuning
Zihao Fu, Haoran Yang, Anthony Man-Cho So +3
Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always…
cs.CL2022★ 1 cited
Parameter-Efficient Tuning by Manipulating Hidden States of Pretrained Language Models For Classification Tasks
Haoran Yang, Piji Li, Wai Lam
Parameter-efficient tuning aims to distill knowledge for downstream tasks by optimizing a few introduced parameters while freezing the pretrained language models (PLMs). Continuous…
cs.CL2022
Improving Lexical Embeddings for Robust Question Answering
Weiwen Xu, Bowei Zou, Wai Lam +1
Recent techniques in Question Answering (QA) have gained remarkable performance improvement with some QA models even surpassed human performance. However, the ability of these mode…