Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression
To excel at their domain large language models are comprised of billions of parameters. Yet this comes at the cost of huge memory requirements restricting their applicability in resource-constrained environments. To address the problem of neural network (NN) compression Singular Value Decomposition (SVD) has played a key role as a fundamental component for matrix compression through decomposition. To minimize compression error and to maximize the efficacy of the compressed model on the downstream tasks previous works focused on low-rank approximation of the NN's weight matrices either from the perspective of parameter importance or per-layer functional equivalence. While previous works st...
| Authors | Athanasios Ntovas, Alexandros Doumanoglou, Petros Drakoulis et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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