| Challenge: | Admin (Adaptive model initialization) is more stable, converges faster, and leads to better performance. |
| Approach: | They propose a model initialization algorithm to stabilize early training and unleash its full potential in the late stage. |
| Outcome: | The proposed model initialization method stabilizes early training and unleashes full potential in late stage. |
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BranchNorm: Robustly Scaling Extremely Deep Transformers (2024.findings-acl)
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| Challenge: | Recent work on DeepNorm scales Transformers into extremely deep (1000 layers) due to the training instability of Transformers, the depths of these SOTA models are still relatively shallow. |
| Approach: | They propose a branch-rescaled model which dynamically rescales the non-residual branch of Transformer in accordance with the training period. |
| Outcome: | The proposed approach significantly outperforms existing shallow models on multiple translation tasks and achieves better training stability and convergent performance. |
BERT Busters: Outlier Dimensions that Disrupt Transformers (2021.findings-acl)
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| Challenge: | Existing studies show that pre-trained Transformers are remarkably robust to pruning. |
| Approach: | They show that pre-trained Transformer encoders are surprisingly fragile to pruning . they show that disabling them significantly degrades both the MLM loss and the downstream task performance. |
| Outcome: | The results show that the removal of features in pre-trained transformers significantly degrades both the MLM loss and the downstream task performance. |
Analyzing Redundancy in Pretrained Transformer Models (2020.emnlp-main)
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| Challenge: | Recent work shows that transformer-based deep NLP models are over-parameterized and do not require all the representational power lent by the rich architectural choices during inference. |
| Approach: | They define a notion of Redundancy and propose a feature-based transfer learning procedure which maintains 97% performance while using at-most 10% of the original neurons. |
| Outcome: | The proposed model maintains 97% performance while using 10% of the original neurons. |
Exploring Quantization for Efficient Pre-Training of Transformer Language Models (2024.findings-emnlp)
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| Challenge: | Quantization has proven to be effective after pre-training and during fine-tuning, but its effects on pre-trainer performance have remained unexplored. |
| Approach: | They propose a linear quantization strategy to be applied during the pre-training of Transformers to improve model efficiency and stability. |
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Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
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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. |
| Outcome: | The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins. |
Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021.emnlp-main)
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| Challenge: | Recent advances in transformer quantization have shown remarkable improvement in many Natural Language Processing tasks and beyond. |
| Approach: | They propose a novel quantization scheme for transformers that can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss. |
| Outcome: | The proposed methods achieve state-of-the-art results on the GLUE benchmark using BERT, while preserving memory and accuracy. |
B2T Connection: Serving Stability and Performance in Deep Transformers (2023.findings-acl)
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| Challenge: | Existing methods to prevent the vanishing gradient problem in deep neural networks are not effective. |
| Approach: | They propose a method that can equip both higher stability and effective training by a simple modification from Post-LN. |
| Outcome: | The proposed method outperforms Pre-LN and Post-Ln on a wide range of tasks. |
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent (2021.emnlp-main)
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| Challenge: | Evidence is emerging that neural networks learn due to inductive bias in the training routine, typically a variant of gradient descent (GD). |
| Approach: | They propose to characterize GD as an inductive bias in transformer training . they document norm growth in transformer language models and show they are saturated . |
| Outcome: | Empirically, we document norm growth in the training of transformer language models . the results suggest saturation is a new characterization of an inductive bias implicit in GD . |
Optimizing Deeper Transformers on Small Datasets (2021.acl-long)
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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao
| Challenge: | a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets. |
| Approach: | They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch . |
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models (2024.findings-acl)
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| Challenge: | Existing studies show that fine-tuned textual transformer models are vulnerable to adversarial text perturbations. |
| Approach: | They extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict adversarial robustness of the fine- tuned models. |
| Outcome: | The proposed framework can be used as an additional tool for robustness evaluation since it saves 30x-193x runtime compared to the traditional technique and can be easily used under adversarial training. |