SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression

• URL: http://arxiv.org/abs/2412.12204v1
• Date: Sun, 15 Dec 2024 12:01:43 GMT
• Title: SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
• Authors: Jing Zhang, Shuzhen Sun, Peng Zhang, Guangxing Cao, Hui Gao, Xindian Ma, Nan Xu, Yuexian Hou,
• Abstract summary: Transformer-based large language models exhibit groundbreaking capabilities, but their storage and computational costs are 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.
• Score: 20.824040486029354
• License:
• Abstract: 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.

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