Papers by Yulia Kuzkina
From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction (2026.acl-industry)
Copied to clipboard
Egor Maximov, Yulia Kuzkina, Egor Shvetsov, Azamat Kanametov, Aleksandr Prutko, Maxim Zhelnin, Aleksei Goncharov
| Challenge: | Quantization and sparsification are important for large language models, but they struggle to meet performance thresholds due to limited flexibility and sensitivity to outlier weights. |
| Approach: | They propose to use 8:16 semi-structured sparsity to surpass performance thresholds . they also show that structured sparsification for outliers is competitive with unstructured approaches . |
| Outcome: | The proposed method surpasses the Performance Threshold, compared to 2:4 sparsity, and offers greater flexibility with minimal storage overhead. |