Summary of A Comprehensive Survey Of Compression Algorithms For Language Models, by Seungcheol Park et al.
A Comprehensive Survey of Compression Algorithms for Language Models
by Seungcheol Park, Jaehyeon Choi, Sojin Lee, U Kang
First submitted to arxiv on: 27 Jan 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 surveys diverse compression algorithms for language models to balance remarkable advances with side effects like increased carbon emissions and expensive maintenance fees. It summarizes various approaches including pruning, quantization, knowledge distillation, low-rank approximation, parameter sharing, and efficient architecture design. The authors provide in-depth analyses of representative algorithms and discuss the value of each category. They also introduce promising future research topics based on their survey results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models smaller without losing accuracy. Right now, language models are really big, which makes them hard to use because they take up a lot of space and energy. The authors looked at many different ways to make these models smaller, including cutting out parts that aren’t important, changing the way numbers are stored, and using simpler models. They also explained what each method does well and what it doesn’t do so well. Finally, they suggested some new areas for researchers to explore. |
Keywords
» Artificial intelligence » Knowledge distillation » Pruning » Quantization
