| Challenge: | Knowledge distillation (KD) is a powerful model compression technique for deep neural networks. |
| Approach: | They propose a method to feed the rich information provided by teacher’s soft-targets incrementally and more efficiently by annealing the teacher output incrementally. |
| Outcome: | The proposed method can be used on image classification and NLP language inference tasks with BERT-based models on the GLUE benchmark. |
Similar Papers
Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing knowledge distillation techniques for neural machine translation lack special treatment on the top-1 information, which is limiting the potential of KD. |
| Approach: | They propose a method to distill knowledge from top-1 predictions of teachers and a technique to infuse more additional knowledge by distilling on the data without ground-truth targets. |
| Outcome: | The proposed method outperforms the vanilla word-level KD and outperfies the existing methods on three different students with different capacity gaps. |
Towards Zero-Shot Knowledge Distillation for Natural Language Processing (2021.emnlp-main)
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| Challenge: | Knowledge distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions. |
| Approach: | They propose to use teacher training data for model compression . they investigate six tasks and find they can achieve between 75% and 92% of the teacher’s classification score while compressing the model 30 times. |
| Outcome: | The proposed solution achieves between 75% and 92% of the teacher’s classification score while compressing the model 30 times. |
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation techniques are not suitable for deep learning tasks due to memory constraints. |
| Approach: | They propose to combine knowledge from a large teacher network into a student network (S) they propose to use a combinatorial mechanism to inject layer-level supervision from T to S . |
| Outcome: | The proposed model outperforms existing models in PortugueseEnglish, TurkishEnglish and EnglishGerman directions and students trained using it have 50% fewer parameters and can deliver comparable results to 12-layer teachers. |
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models (2023.acl-short)
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Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen
| Challenge: | Knowledge distillation (KD) is an effective compression technique to derive a smaller student model from a larger teacher model by transferring the knowledge embedded in the teacher's network. |
| Approach: | They propose a framework and loss function that preserves the semantic similarities of teacher and student training examples to enable the student to retrieve from the knowledge base effectively. |
| Outcome: | The proposed framework preserves the semantic similarities of teacher and student training examples to achieve state-of-the-art performance on the GLUE benchmark. |
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization (2022.findings-emnlp)
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| Challenge: | Existing methods for knowledge distillation (KD) do not mitigate the noise in the teacher’s output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. |
| Approach: | They propose a method that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. |
| Outcome: | The proposed method achieves state-of-the-art performance on NLU and computer vision tasks. |
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information. |
| Approach: | They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark and shows that it is more efficient than existing methods. |
Knowledge Distillation for Language Models (2025.naacl-tutorial)
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| Challenge: | Knowledge distillation (KD) aims to transfer knowledge from a teacher to a student . this tutorial will cover topics ranging from LLM sequence compression to LLM self-distillation . |
| Approach: | They propose to introduce intermediate-layer matching and prediction matching . they will then present advanced techniques such as reinforcement learning-based KD and multi-teacher distillation . |
| Outcome: | This tutorial aims to provide participants with a comprehensive understanding of the techniques and applications of knowledge distillation for language models. |
Cost-effective Distillation of Large Language Models (2023.findings-acl)
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| 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. |
Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing knowledge distillation methods only obtain one lightweight student each time . this could be resource-intensive and resulting in multiple students not being optimally utilized . |
| Approach: | They propose a knowledge distillation framework which generates multiple satisfactory students at once. |
| Outcome: | The proposed framework generates multiple satisfactory students at once. |
BAM! Born-Again Multi-Task Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing methods to train multi-task neural networks outperform or even match their single-task counterparts are difficult to implement. |
| Approach: | They propose a method that uses knowledge distillation to train multi-task neural networks that outperform or even match their single-task counterparts. |
| Outcome: | The proposed method outperforms or matches single-task neural networks on the GLUE benchmark. |