Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings (D19-61)
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| Challenge: | Recent research points to knowledge distillation as a potential solution for NLU tasks. |
| Approach: | They propose a training approach that distills large finetuned LMs into a small network using unlabeled training examples. |
| Outcome: | The proposed approach outperforms BERT training approaches while using 300 times fewer parameters. |
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Natural Language Generation for Effective Knowledge Distillation (D19-61)
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| Challenge: | Knowledge distillation can transfer knowledge from deep language representation models to shallow word embedding-based neural networks. |
| Approach: | They propose to build an unlabeled transfer dataset to enable effective knowledge transfer . they hypothesize that this principled, general approach outperforms rule-based techniques . |
| Outcome: | The proposed method outperforms OpenAI GPT on four datasets in sentiment classification, sentence similarity, and linguistic acceptability. |
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)
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| 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. |
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Beyond One-Step Distillation: Bridging the Capacity Gap in Small Language Models via Multi-Step Knowledge Transfer (2026.eacl-srw)
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| Challenge: | Large Language Models (LLMs) excel across diverse tasks but remain too large for efficient on-device deployment. |
| Approach: | They revisit multi-step knowledge distillation as an effective remedy . they demonstrate that MSKD improves ROUGE-L and perplexity over single-step approaches . |
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (2023.findings-acl)
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister
| Challenge: | Deploying large language models (LLMs) is difficult because they are memory inefficient and compute-intensive for practical applications. |
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Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection (2022.findings-emnlp)
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| Challenge: | Existing studies on knowledge distillation have shown that not all knowledge is necessary for learning a good student model. |
| Approach: | They propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. |
| Outcome: | The proposed method outperforms several strong knowledge distillation baselines significantly on the GLUE datasets. |
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. |
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. |
TinyBERT: Distilling BERT for Natural Language Understanding (2020.findings-emnlp)
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| 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. |
Basic Reading Distillation (2025.acl-long)
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| Challenge: | Large language models require high computational resources which limits their deployment in real-world applications. |
| Approach: | They propose to distill large language models into smaller language models by either knowledge distillation or task distillation. |
| Outcome: | The proposed model outperforms or performs comparable to over 20x bigger LLMs on language inference benchmarks and BIG-bench tasks. |
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models (2020.acl-main)
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| Challenge: | Existing work on pre-trained language models focuses on reducing the size of these models into shallow ones. |
| Approach: | They propose a knowledge distillation technique that leverages teacher internal representations to reduce the size of pre-trained language models. |
| Outcome: | The proposed method outperforms previous methods in multilingual Named Entity Recognition (NER) it reduces the size of teacher models by 35x while retaining 95% of its F1 score. |