SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916526741782528 |
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| author | Zhang, Jing Sun, Shuzhen Zhang, Peng Cao, Guangxing Gao, Hui Ma, Xindian Xu, Nan Hou, Yuexian |
| author_facet | Zhang, Jing Sun, Shuzhen Zhang, Peng Cao, Guangxing Gao, Hui Ma, Xindian Xu, Nan Hou, Yuexian |
| contents | Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resource-constrained scenarios. An effective approach is to eliminate redundant model parameters and computational costs while incorporating efficient expert-derived knowledge structures to achieve a balance between compression and performance. Therefore, we propose the \textit{Sememe Entanglement Encoding (SEE)} algorithm. Guided by expert prior knowledge, the model is compressed through the low-rank approximation idea. In Entanglement Embedding, basic semantic units such as sememes are represented as low-dimensional vectors, and then reconstructed into high-dimensional word embeddings through the combination of generalized quantum entanglement. We adapt the Sememe Entanglement Encoding algorithm to transformer-based models of different magnitudes. Experimental results indicate that our approach achieves stable performance while compressing model parameters and computational costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression Zhang, Jing Sun, Shuzhen Zhang, Peng Cao, Guangxing Gao, Hui Ma, Xindian Xu, Nan Hou, Yuexian Machine Learning Artificial Intelligence Computation and Language Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are prohibitively high, limiting their application in resource-constrained scenarios. An effective approach is to eliminate redundant model parameters and computational costs while incorporating efficient expert-derived knowledge structures to achieve a balance between compression and performance. Therefore, we propose the \textit{Sememe Entanglement Encoding (SEE)} algorithm. Guided by expert prior knowledge, the model is compressed through the low-rank approximation idea. In Entanglement Embedding, basic semantic units such as sememes are represented as low-dimensional vectors, and then reconstructed into high-dimensional word embeddings through the combination of generalized quantum entanglement. We adapt the Sememe Entanglement Encoding algorithm to transformer-based models of different magnitudes. Experimental results indicate that our approach achieves stable performance while compressing model parameters and computational costs. |
| title | SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2412.12204 |