Summary of Survey on Knowledge Distillation For Large Language Models: Methods, Evaluation, and Application, by Chuanpeng Yang et al.
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
by Chuanpeng Yang, Wang Lu, Yao Zhu, Yidong Wang, Qian Chen, Chenlong Gao, Bingjie Yan, Yiqiang Chen
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey on knowledge distillation techniques specifically designed for Large Language Models (LLMs), focusing on methods, evaluations, and applications. The authors divide knowledge distillation into white-box and black-box approaches to illustrate their differences, exploring the effects of different distillation methods on evaluation tasks and proposing future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make Large Language Models smaller and faster without losing their abilities. Researchers have been trying to find ways to “compress” language models so they can be used in places with limited resources. One way to do this is by using a technique called knowledge distillation, which helps speed up language model inference while keeping performance high. This paper looks at different methods for doing this and how well they work. |
Keywords
» Artificial intelligence » Distillation » Inference » Knowledge distillation » Language model
