A new research approach improves low-rank SVD-based LLM compression by replacing global rank allocation with layer-wise error model search, addressing what the authors identify as two key limitations in existing methods: uncalibrated error proxies that fail to capture complex error propagation, and decomposition quality issues. The work targets reducing the computational costs of large language models through low-rank singular value decomposition.
Low-rank SVD-based compression offers a powerful strategy to reduce the computational costs of LLMs. However, existing methods face two key limitations: (i) global rank allocation, where uncalibrated error proxies fail to capture complex error propagation, and (ii) decomposition quality, where Fishe
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