Add to ORKGTransformer-based pre-trained models with millions of parameters require large storage. Recent approaches tackle this shortcoming by training adapters, but these approaches still require a relatively large number of parameters. In this study, AdapterBias, a surprisingly simple yet effective adapter architecture, is proposed. AdapterBias adds a token-dependent shift to the hidden output of transformer layers to adapt to downstream tasks with only a vector and a linear layer. Extensive experiments are conducted to demonstrate the effectiveness of AdapterBias. The experiments show that our proposed method can dramatically reduce the trainable parameters compared to the previous works with a minimal decrease in task performances compared with f...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to ...
Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains re...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting...
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset ...
Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretra...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Massively pre-trained transformer models such as BERT have gained great success in many downstream N...
When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict...
Transformers are responsible for the vast majority of recent advances in natural language processing...
There are growing interests in adapting large-scale language models using parameter-efficient fine-t...
The current modus operandi in adapting pre-trained models involves updating all the backbone paramet...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to ...
Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains re...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting...
We introduce BitFit, a sparse-finetuning method where only the bias-terms of the model (or a subset ...
Parameter-efficient tuning aims at updating only a small subset of parameters when adapting a pretra...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Massively pre-trained transformer models such as BERT have gained great success in many downstream N...
When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict...
Transformers are responsible for the vast majority of recent advances in natural language processing...
There are growing interests in adapting large-scale language models using parameter-efficient fine-t...
The current modus operandi in adapting pre-trained models involves updating all the backbone paramet...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to ...
Fine-tuning large pre-trained models on downstream tasks has been adopted in a variety of domains re...