Text Representation Distillation via Information Bottleneck Principle (2023.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have recently shown great success in text representation field, however, the high computational cost and high-dimensional representation of PLMs pose significant challenges for practical applications. |
| Approach: | They propose a Knowledge Distillation method that distills large models into smaller representation models to reduce performance degradation after distillation. |
| Outcome: | Empirical results on two main downstream applications of the proposed method show that it reduces the risk of over-fitting and maximizes the mutual information between the model and the input data. |
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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. |
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Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) face high computational demands at inference time due to high computational costs. |
| Approach: | They propose a cost-effective and high-throughput solution for large language models . PGKD distills the knowledge of LLMs into smaller, task-specific models based on teacher-student knowledge distillation . |
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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. |
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. |
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Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data (2025.findings-naacl)
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Juanhui Li, Sreyashi Nag, Hui Liu, Xianfeng Tang, Sheikh Muhammad Sarwar, Limeng Cui, Hansu Gu, Suhang Wang, Qi He, Jiliang Tang
| Challenge: | Large Language Models (LLMs) have demonstrated superior language understanding abilities in many real-world NLP applications. |
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| Outcome: | The proposed method improves model performance across datasets with higher data efficiency. |
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. |
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model (2023.acl-industry)
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| Challenge: | Existing knowledge distillation frameworks for language models are limited by memory and the use of complex distillation methods on larger-scale PLMs. |
| Approach: | They propose a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. |
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EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation focus on direct output alignment, neglecting this crucial structural information. |
| Approach: | They propose a framework for knowledge distillation that maps tokens one-to-one and aligns attention matrix patterns using Centered Kernel Alignment. |
| Outcome: | The proposed framework significantly outperforms existing CTKD baselines. |
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. |
Textual Dataset Distillation via Language Model Embedding (2024.findings-emnlp)
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| Challenge: | prevailing methods for dataset distillation generate distilled data as embedding vectors, which are not human-readable. |
| Approach: | They propose a model-agnostic, data-efficient method that leverages Language Model embeddings . their method offers enhanced flexibility and improved transferability . |
| Outcome: | The proposed method achieves comparable performance with faster processing times compared to other methods . it offers enhanced flexibility and improved transferability, expanding the range of potential applications . |