SEE: Sememe Entanglement Encoding for Transformer-bases Models Compression
This addresses the problem of deploying large language models in resource-constrained scenarios, representing an incremental improvement in model compression techniques.
The paper tackles the high storage and computational costs of transformer-based large language models by proposing the Sememe Entanglement Encoding (SEE) algorithm, which compresses models using low-rank approximation and expert knowledge, achieving stable performance with reduced parameters and costs.
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.