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Title: Parameter-Efficient Tuning with Special Token Adaptation
Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretrained model to downstream tasks. In this work, we introduce PASTA, in which we only modify the special token representations (e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer in Transformer-based models. PASTA achieves comparable performance to fine-tuning in natural language understanding tasks including text classification and NER with up to only 0.029% of total parameters trained. Our work not only provides a simple yet effective way of parameter-efficient tuning, which has a wide range of practical applications when deploying finetuned models for multiple tasks, but also demonstrates the pivotal role of special tokens in pretrained language models.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ;
Vlachos, Andreas; Augenstein, Isabelle
Date Published:
Journal Name:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL)
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
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