PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning (2024.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. |
| Approach: | They propose a method that utilizes prompt tuning to enable generative language models to transfer student-friendly knowledge. |
| Outcome: | Extensive experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher’s parameters as prompts. |
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| Challenge: | Knowledge distillation (KD) enables the compression of large language models (LLMs) conventional methods suffer from training-inference mismatches and suboptimal performance due to expensive student-generated outputs. |
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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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| Challenge: | Existing knowledge distillation approaches for language models have overlooked the difficulty of training examples. |
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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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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. |
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A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training (2023.acl-long)
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| Challenge: | Modern Natural Language Generation models come with massive computational and storage requirements. |
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Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition (2024.acl-srw)
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| Challenge: | Trace-of-Thought Prompting allows small neural networks to emulate larger, teacher models with reduced computational demands. |
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Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains (2021.acl-long)
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| Challenge: | Pre-trained language models have been successful in NLP tasks, but their large size and long inference time limit their deployment in real-time applications. |
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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. |
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C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
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