| Challenge: | Existing knowledge distillation methods cannot be directly applied to train student models with reduced vocabulary and embedding dimensions. |
| Approach: | They propose a method to align teacher and student embeddings via mixed-vocabulary training. |
| Outcome: | The proposed method compresses BERT-LARGE to a task-agnostic model with smaller vocabulary and hidden dimensions, which is an order of magnitude smaller than other distilled models. |
Similar Papers
Patient Knowledge Distillation for BERT Model Compression (D19-1)
Copied to clipboard
| Challenge: | Pre-trained language models such as BERT have proven to be highly effective for natural language processing tasks, but the high demand for computing resources hinders their application in practice. |
| Approach: | They propose to compress an original large model (teacher) into an equally-effective lightweight shallow network (student) Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy. |
| Outcome: | The proposed model reduces the computational cost of training models using the teacher model into a lightweight shallow network. |
TinyBERT: Distilling BERT for Natural Language Understanding (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models are computationally expensive and difficult to efficiently execute on resource-restricted devices. |
| Approach: | They propose a Transformer distillation method that performs Transformer distillations at pre-training and task-specific learning stages. |
| Outcome: | The proposed method accelerates inference and reduces model size while maintaining accuracy. |
Cost-effective Distillation of Large Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge distillation methods require pretraining of the teacher on task-specific datasets, which can be costly for large and unstable for small datasets. |
| Approach: | They propose an approach to improve knowledge distillation by a loss-agnostic approach to task and model architecture. |
| Outcome: | The proposed method achieves competitive results across a range of tasks, especially for tasks with smaller datasets. |
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements. |
| Approach: | They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets. |
| Outcome: | The proposed approach improves on the SST-2, MRPC, YELP-2, and TREC-6 datasets. |
Distilling Knowledge Learned in BERT for Text Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Large-scale pre-trained language models such as BERT have revolutionized the state of the art in many language understanding tasks. |
| Approach: | They propose a conditional masked language modeling approach to fine tune BERT on target generation tasks by imposing global sequence-level supervision on conventional Seq2Seq models. |
| Outcome: | The proposed model outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization. |
Maximizing the Effectiveness of Larger BERT Models for Compression (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for capturing large BERT models as teachers do not fully exploit the potential advantages of larger teachers. |
| Approach: | They propose a method that leverages a pretrained teacher model to guide the training of a lightweight student model to enhance knowledge transfer. |
| Outcome: | The proposed method enhances knowledge transfer by leveraging a pretrained teacher model to guide the training of a lightweight student model. |
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)
Copied to clipboard
Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai
| Challenge: | Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive . |
| Approach: | They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model. |
| Outcome: | The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy. |
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to reduce inference cost by distilling transformer models into lightweight student models are limited for high-volume use cases. |
| Approach: | They propose to distill state-of-the-art transformer models into lightweight student models to reduce computation cost at inference time. |
| Outcome: | The proposed pipeline achieves up to 600x speed-up on GPUs and CPUs on six single-sentence text classification tasks and in domain generalization settings. |
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers (2022.lrec-1)
Copied to clipboard
Andrei-Marius Avram, Darius Catrina, Dumitru-Clementin Cercel, Mihai Dascalu, Traian Rebedea, Vasile Pais, Dan Tufis
| Challenge: | Existing approaches to train pre-trained language models focus on the English language, thus widening the gap when considering low-resource languages. |
| Approach: | They propose three versions of distilled BERT models for the Romanian language . they argue that the models offer performance comparable to their teachers . |
| Outcome: | The proposed models perform comparable to their teachers, while being twice as fast on a GPU and 35% smaller. |
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods for transferring knowledge from BERT into a model with large parameters are not efficient due to their large-scale and high computational cost. |
| Approach: | They propose a sentence representation approximating oriented distillation framework that can distill pre-trained BERT into a simple LSTM based model without specifying tasks. |
| Outcome: | The proposed model outperforms other distillation methods and larger models on multiple NLP tasks with efficiency well-improved. |