Summary of What Happens When Small Is Made Smaller? Exploring the Impact Of Compression on Small Data Pretrained Language Models, by Busayo Awobade et al.
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models
by Busayo Awobade, Mardiyyah Oduwole, Steven Kolawole
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 explores the application of pruning, knowledge distillation, and quantization to AfriBERTa, a low-resource language model trained on small amounts of data. The authors examine how these compression techniques impact performance across various metrics beyond accuracy, aiming to understand their effectiveness in improving efficiency for small-data models. By leveraging these techniques, the study demonstrates that compression can significantly enhance the performance and efficiency of low-resource language models, mirroring the findings for large-scale models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make a special kind of computer program called AfriBERTa work more efficiently with limited data. AfriBERTa is trained on small amounts of information and needs help to process it faster. The researchers test different techniques like cutting out unnecessary parts, sharing knowledge, and reducing data sizes. They want to see how these methods affect the performance of AfriBERTa beyond just making it correct. The results show that these techniques can greatly improve the speed and quality of AfriBERTa’s work. |
Keywords
» Artificial intelligence » Knowledge distillation » Language model » Pruning » Quantization
