Parameter-Efficient Tuning with Special Token Adaptation

• URL: http://arxiv.org/abs/2210.04382v1
• Date: Mon, 10 Oct 2022 01:02:51 GMT
• Title: Parameter-Efficient Tuning with Special Token Adaptation
• Authors: Xiaoocong Yang, James Y. Huang, Wenxuan Zhou, Muhao Chen
• Abstract summary: PASTA achieves comparable performance to fine-tuning in natural language understanding tasks. Our work demonstrates the pivotal role of special tokens in pretrained language models.
• Score: 25.37998979962568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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