Summary of Wave Network: An Ultra-small Language Model, by Xin Zhang et al.
Wave Network: An Ultra-Small Language Model
by Xin Zhang, Victor S.Sheng
First submitted to arxiv on: 4 Nov 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 proposed Wave network is an ultra-small language model that uses complex vectors to represent tokens, incorporating both global and local semantics of input text. The complex vector consists of magnitude and phase components, which enable the Wave network to outperform a single Transformer layer using BERT pre-trained embeddings in text classification tasks. Specifically, the Wave network achieves 90.91% accuracy with wave interference and 91.66% with wave modulation, surpassing the accuracy of a single Transformer layer by 19.23% and 19.98%, respectively. Furthermore, the Wave network reduces video memory usage and training time compared to BERT base. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Wave network is a new language model that can process text information quickly and accurately. It works by using special vectors to represent each word or token in the text, taking into account both what the word means globally and how it relates to other words. This helps the model understand the relationships between words better than previous models. In tests, the Wave network was able to classify text as accurate as a much larger model called BERT, but using much less memory and computing power. |
Keywords
» Artificial intelligence » Bert » Language model » Semantics » Text classification » Token » Transformer
