Parameter-Efficient Tuning with Special Token Adaptation
Xiaocong Yang, James Y. Huang, Wenxuan Zhou, Muhao Chen · 2023
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 full finetuning 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. 1