Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models . |
| Approach: | They propose a method to estimate logits from the decision distributions using logits theoretically and empirically. |
| Outcome: | The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets. |
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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. |
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Pre-training Distillation for Large Language Models: A Design Space Exploration (2025.acl-long)
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| Challenge: | Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model for model compression. |
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Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs (2025.acl-long)
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Anshumann Anshumann, Mohd Abbas Zaidi, Akhil Kedia, Jinwoo Ahn, Taehwak Kwon, Kangwook Lee, Haejun Lee, Joohyung Lee
| Challenge: | Knowledge distillation is a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. |
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Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment (2025.coling-main)
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| Challenge: | Existing knowledge distillation techniques for large language models are causing difficulties for student models to learn multi-modal probability distributions. |
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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 . |
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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
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| Challenge: | Knowledge distillation (KD) is a key technique for compressing large language models into smaller ones while preserving performance. |
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Beyond Logits: Aligning Feature Dynamics for Effective Knowledge Distillation (2025.acl-long)
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Guoqiang Gong, Jiaxing Wang, Jin Xu, Deping Xiang, Zicheng Zhang, Leqi Shen, Yifeng Zhang, JunhuaShu JunhuaShu, ZhaolongXing ZhaolongXing, Zhen Chen, Pengzhang Liu, Ke Zhang
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Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network (2022.emnlp-main)
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| Challenge: | Existing knowledge distillation approaches for language models have overlooked the difficulty of training examples. |
| Approach: | They propose a framework that controls difficulty of training examples during pre-training by a tutor network. |
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BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation (2025.coling-main)
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| Challenge: | Knowledge distillation (KD) is a method for reducing model size while preserving performance. |
| Approach: | They propose a method to distill large language models at the logit level by transferring knowledge from a large teacher model to a smaller student model. |
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