| Challenge: | Existing studies show that distilled versions of pretrained models are not always available. |
| Approach: | They propose to use distilled versions of successor models as acquisition models to reduce the training cost of the model. |
| Outcome: | The proposed approach reduces the training cost of the model and does not cause the acquisition-successor mismatch (ASM) problem. |
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On the Importance of Effectively Adapting Pretrained Language Models for Active Learning (2022.acl-short)
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| 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. |
Adaptable Adapters (2022.naacl-main)
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| Challenge: | Existing work uses the same adapter architecture for every dataset regardless of the properties of the dataset or the amount of training data. |
| Approach: | They propose to use adaptable adapters to finetune lightweight neural network layers on top of pretrained weights. |
| Outcome: | The proposed adapters achieve on-par performances with the standard adapter architecture while using a considerably smaller number of adapter layers. |
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
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| Challenge: | Existing approaches to deep learning for NLP require large amounts of labeled data. |
| Approach: | They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model. |
| Outcome: | The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead. |
Towards Computationally Feasible Deep Active Learning (2022.findings-naacl)
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Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Active learning (AL) is a technique for reducing the amount of annotation required for training machine learning models. |
| Approach: | They propose two techniques that reduce the amount of time required for AL . they use pseudo-labeling and distilled models to train a successor model . |
| Outcome: | The proposed algorithm reduces the time and computational overhead required to train an acquisition model and estimate uncertainty on instances in the unlabeled pool. |
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning (2023.emnlp-demo)
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Clifton Poth, Hannah Sterz, Indraneil Paul, Sukannya Purkayastha, Leon Engländer, Timo Imhof, Ivan Vulić, Sebastian Ruder, Iryna Gurevych, Jonas Pfeiffer
| Challenge: | Adapters is an open-source library that unifies parameter-efficient and modular transfer learning in large language models. |
| Approach: | They propose to integrate 10 different methods into a unified interface for parameter-efficient and modular transfer learning in large language models. |
| Outcome: | The proposed library is able to perform on multiple NLP tasks and is open-source. |
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)
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| Challenge: | a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant . |
| Approach: | They conduct an online survey to assess the perceived relevance of data annotation and active learning . they propose a strategy to reduce annotation costs using active learning, an iterative process . |
| Outcome: | The proposed strategies reduce setup complexity and uncertainty cost while maintaining model performance. |
Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)
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| 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. |
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)
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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. |
What to Pre-Train on? Efficient Intermediate Task Selection (2021.emnlp-main)
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| Challenge: | Existing methods for fine-tuning intermediate tasks are inefficient and expensive. |
| Approach: | They propose to use a set of 42 intermediate and 11 target English classification, multiple choice, question answering, and sequence tagging tasks to identify the best settings for intermediate transfer learning. |
| Outcome: | The proposed methods achieve an average Regret@3 of 1% across all target tasks. |
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers (2024.emnlp-main)
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| Challenge: | Existing studies show that augmenting the training data of pre-trained language models with parametric fine-tuning methods can enhance their robustness under adversarial attacks. |
| Approach: | They propose an approach that fine-tunes PLMs with adapters and adversarial augmentation via mixup to leverage existing knowledge from a set of pre-known attacks. |
| Outcome: | The proposed approach achieves best trade-off between training efficiency and robustness under adversarial attacks compared to baselines on five downstream tasks across six varied black-box attacks and 2 PLMs. |