Summary of See: Sememe Entanglement Encoding For Transformer-bases Models Compression, by Jing Zhang et al.
SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
by Jing Zhang, Shuzhen Sun, Peng Zhang, Guangxing Cao, Hui Gao, Xindian Ma, Nan Xu, Yuexian Hou
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Sememe Entanglement Encoding (SEE) algorithm aims to balance compression and performance for transformer-based large language models, making them more feasible for resource-constrained scenarios. By eliminating redundant parameters and incorporating efficient expert-derived knowledge structures, SEE achieves significant storage and computational cost savings while maintaining model capabilities. The approach involves low-rank approximation and entanglement embedding, where basic semantic units (sememes) are represented as low-dimensional vectors and reconstructed into high-dimensional word embeddings. Experimental results demonstrate the effectiveness of SEE in compressing model parameters and computational costs without sacrificing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SEE algorithm helps make large language models more usable by reducing their storage needs and processing power requirements. It does this by removing unnecessary parts of the model and adding helpful information from experts. This approach, called low-rank approximation, is combined with another technique called entanglement embedding. By using these methods, the model can be made smaller without losing its abilities. The results show that SEE can make language models more efficient while keeping them powerful. |
Keywords
* Artificial intelligence * Embedding * Transformer
