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Transformer models have achieved remarkable results in various naturallanguage tasks, but they are often prohibitively large, requiring massivememories and computational resources. To reduce the size and complexity ofthese models, we propose LoSparse (Low-Rank and Sparse approximation), a novelmodel compression technique that approximates a weight matrix by the sum of alow-rank matrix and a sparse matrix. Our method combines the advantages of bothlow-rank approximations and pruning, while avoiding their limitations. Low-rankapproximation compresses the coherent and expressive parts in neurons, whilepruning removes the incoherent and non-expressive parts in neurons. Pruningenhances the diversity of low-rank approximations, and low-rank approximationprevents pruning from losing too many expressive neurons. We evaluate ourmethod on natural language understanding, question answering, and naturallanguage generation tasks. We show that it significantly outperforms existingcompression methods.

LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation