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cs.LG2026
Importance-Guided Basis Selection for Low-Rank Decomposition of Large Language Models
Daniel Agyei Asante, Ernie Chang, Yang Li
Low-rank decomposition is a compelling approach for compressing large language models, but its effectiveness hinges on selecting which singular-vector bases to retain for a target…
cs.LG2026
IMPACT: Importance-Aware Activation Space Reconstruction
Md Mokarram Chowdhury, Daniel Agyei Asante, Ernie Chang +1
Large language models (LLMs) achieve strong performance across diverse domains but remain difficult to deploy in resource-constrained environments due to their size. Low-rank compr…
cs.LG2025
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Yang Li, Daniel Agyei Asante, Changsheng Zhao +3
Large language models (LLMs) significantly enhance the performance of various applications, but they are computationally intensive and energy-demanding. This makes it challenging t…