5 citations · 7 across the 12 of their papers we have counts for
3 papers · 1 filter
Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization
Janghwan Lee, Minsoo Kim, Seungcheol Baek +3
Large Language Models (LLMs) are proficient in natural language processing tasks, but their deployment is often restricted by extensive parameter sizes and computational demands. T…
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Minsoo Kim, Sihwa Lee, Janghwan Lee +4
Generative Language Models (GLMs) have shown impressive performance in tasks such as text generation, understanding, and reasoning. However, the large model size poses challenges f…
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers
Minsoo Kim, Kyuhong Shim, Seongmin Park +2
Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. Quantization-awa…