Summary of A Survey on Transformers in Nlp with Focus on Efficiency, by Wazib Ansar et al.
A Survey on Transformers in NLP with Focus on Efficiency
by Wazib Ansar, Saptarsi Goswami, Amlan Chakrabarti
First submitted to arxiv on: 15 May 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 advent of transformers with attention mechanisms and pre-trained models has revolutionized Natural Language Processing (NLP), but their resource-intensive nature limits applications in constrained environments. This paper surveys the evolution of NLP, emphasizing both accuracy and efficiency. It investigates research contributions enhancing transformer-based model efficiency at various stages, considering hardware constraints. The goal is to establish a foundation for future research on sustainable NLP techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NLP models have changed the way we process language. They’re super powerful, but they need lots of computing power to work well. This means they can’t be used in places with limited resources. When choosing an NLP model, you have to decide whether you want it to be accurate or efficient. This paper looks at how researchers are working to make these models more efficient, so we can use them in a wider range of situations. |
Keywords
» Artificial intelligence » Attention » Natural language processing » Nlp » Transformer
