| Challenge: | Recent Transformer based models have achieved state-of-the-art performance for many natural language processing tasks including machine translation, question-answering tasks and semantic role labeling. |
| Approach: | They propose to reduce the number of parameters of BERT to obtain a much efficient light model. |
| Outcome: | The proposed model achieves 6.6% higher average accuracy on GLUE and SQuAD datasets than the previous model with three encoder layers while having the same number of parameters. |
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Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
| Challenge: | Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements. |
| Approach: | They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention. |
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (N19-1)
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| Challenge: | Existing language representation models pre-train deep bidirectional representations from unlabeled text without significant task-specific architecture modifications. |
| Approach: | They propose a language representation model that pre-trains bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. |
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A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
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GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)
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| Challenge: | Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation. |
| Approach: | They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance. |
| Outcome: | Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method. |
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection (2022.acl-long)
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| Challenge: | Existing models that use heuristics to shorten sequence lengths are computationally prohibitive. |
| Approach: | They propose a new method to shorten sequence lengths by transforming tokens through encoders and a core-set based token selection method that avoids expensive pre-training and fine tuning. |
| Outcome: | The proposed model outperforms existing models on GLUE benchmarks and Long Range Arena datasets and demonstrates that it is cost-effective and space-efficient. |
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning (2020.coling-main)
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| Challenge: | Recent work has explored the suitability of pre-trained language models in low resource settings with less than 1,000 training data points. |
| Approach: | They propose to use pool-based active learning to speed up training while keeping the cost of labeling new data constant. |
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Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)
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| Challenge: | Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. |
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The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models (2022.emnlp-main)
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Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
| Challenge: | Pre-trained Transformer models provide robust language representations which can be specialized on various tasks. |
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| Outcome: | The proposed method is the first to be applied at the BERT scale and significantly pushes the boundaries of the current sparse models with respect to all metrics: model size, inference speed and task accuracy. |
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT (2022.findings-naacl)
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| Challenge: | Existing methods to fine-tune a model for multiple tasks require a large amount of memory and computing power. |
| Approach: | They propose to factorize the weighs of a pre-trained Transformer model to improve training efficiency across multiple tasks by using BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark. |
| Outcome: | The proposed method matches or improves the original fine-tuned model’s performance for each task while effectively decreasing parameter requirements by two orders of magnitude. |
Training compute-optimal transformer encoder models (2025.emnlp-main)
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| Challenge: | OptiBERT is a family of compute-optimal BERT-style models that matches or surpasses leading baselines while training with dramatically less FLOPS. |
| Approach: | They propose to train OptiBERT models with a Masked Language Model objective . they train a family of compute-optimal BERT-style models that matches or surpasses leading baselines . |
| Outcome: | The proposed model matches or surpasses leading baselines on GLUE and MTEB while training with dramatically less FLOPS. |