Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial. |
| Approach: | They propose a simple yet effective adapter-based approach to fine-tune language models on downstream tasks. |
| Outcome: | The proposed approach improves stability and adversarial robustness in transfer learning to various downstream tasks. |
Similar Papers
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
Copied to clipboard
| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
| Outcome: | The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty. |
Investigating Transferability in Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on deep NLP models has centered on probing, a method that involves training classifiers for different tasks on model representations. |
| Approach: | They propose a method for determining the impact of each pretrained layer on transfer task performance by ablation. |
| Outcome: | The proposed method shows that pretraining models improve performance on downstream tasks . the results highlight the limitations of methods that operate on frozen models or single data samples. |
On Robustness of Finetuned Transformer-based NLP Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained Transformer-based language models have been finetuned for a large number of tasks. |
| Approach: | They characterize changes between pretrained and finetuned models with CKA and STIR metrics. |
| Outcome: | The proposed models are more robust to perturbations than BERT and T5 on classification tasks and generation tasks. |
Multi-Stage Pre-training for Low-Resource Domain Adaptation (2020.emnlp-main)
Copied to clipboard
Rong Zhang, Revanth Gangi Reddy, Md Arafat Sultan, Vittorio Castelli, Anthony Ferritto, Radu Florian, Efsun Sarioglu Kayi, Salim Roukos, Avi Sil, Todd Ward
| Challenge: | Existing approaches to transfer learning target data to in-domain text . prior work has adapted pre-trained LMs to specific domains . |
| Approach: | They extend the vocabulary of a pretrained language model with domain-specific terms to create synthetic tasks that help it transfer to downstream tasks. |
| Outcome: | The proposed approaches show significant performance gains on extractive reading comprehension, document ranking and duplicate question detection tasks. |
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained models fail to generalize to unseen data. |
| Approach: | They propose a framework for robust and efficient fine-tuning for pre-trained models . proposed framework achieves new state-of-the-art performance on a number of NLP tasks . |
| Outcome: | The proposed framework outperforms the state-of-the-art T5 model on GLUE, SNLI, SciTail and ANLI. |
AdapterHub: A Framework for Adapting Transformers (2020.emnlp-demos)
Copied to clipboard
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, Iryna Gurevych
| Challenge: | AdapterHub framework enables dynamic “stiching-in” of pre-trained adapters for different tasks and languages. |
| Approach: | They propose a framework that allows dynamic "stiching-in" of pre-trained adapters for different tasks and languages. |
| Outcome: | The proposed framework allows dynamic “stiching-in” of pre-trained adapters for different tasks and languages. |
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
Copied to clipboard
| Challenge: | Recent active learning approaches in NLP use off-the-shelf pretrained language models (LMs) . a poor training strategy can be catastrophic for AL, authors argue . |
| Approach: | They propose to first adapt the pretrained LM to the target task and then use it for AL. |
| Outcome: | The proposed approach provides substantial data efficiency improvements compared to the standard fine-tuning approach. |
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems. |
| Approach: | They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism. |
| Outcome: | The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
Copied to clipboard
Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si
| Challenge: | Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning. |
| Approach: | They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task. |
| Outcome: | The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings. |
Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection (2023.findings-eacl)
Copied to clipboard
| Challenge: | AfriBERTa shows that training transformer models from scratch on 1GB of data from many unrelated African languages outperforms massively multilingual models on downstream NLP tasks. |
| Approach: | They propose that training on smaller amounts of data but from related languages could match the performance of models trained on large, unrelated data. |
| Outcome: | The proposed model outperforms models trained on large, unrelated datasets on downstream NLP tasks. |