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. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
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| Challenge: | Existing knowledge distillation techniques are not suitable for deep learning tasks due to memory constraints. |
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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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| Challenge: | Existing large language models (LLMs) have strong generalization abilities due to their huge model capacities. |
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| Challenge: | Large Language Models (LLMs) have shown impressive emergent capabilities by leveraging Chain-of-Thought (CoT) prompting. |
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| Challenge: | Large Language Models (LLMs) excel across diverse tasks but remain too large for efficient on-device deployment. |
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TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)
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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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Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive. |
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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. |
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