Summary of On Importance Of Pruning and Distillation For Efficient Low Resource Nlp, by Aishwarya Mirashi et al.
On Importance of Pruning and Distillation for Efficient Low Resource NLP
by Aishwarya Mirashi, Purva Lingayat, Srushti Sonavane, Tejas Padhiyar, Raviraj Joshi, Geetanjali Kale
First submitted to arxiv on: 21 Sep 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 A novel approach to natural language processing tackles the challenge of scaling transformer-based models while minimizing computational resources. The study focuses on developing efficient techniques for smaller models, building upon previous work like Distilbert and MobileBert, which aimed to accelerate English models. However, there is a lack of research in this area for low-resource languages. This paper bridges this gap by introducing innovative strategies to reduce the size and training time of transformer models while maintaining their performance on tasks such as text classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make language processing better for less common languages. Right now, big AI models are really good at understanding English, but they’re not as good with smaller languages. This makes it harder for people who speak those languages to use the same technology. The researchers want to change this by making a smaller version of these models that can work faster and more efficiently, while still being just as good. |
Keywords
» Artificial intelligence » Natural language processing » Text classification » Transformer
