701 citations · 1.1k across the 18 of their papers we have counts for
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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…
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders
Minsoo Kim, Sihwa Lee, Sukjin Hong +2
Knowledge distillation (KD) has been a ubiquitous method for model compression to strengthen the capability of a lightweight model with the transferred knowledge from the teacher.…
Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling
Kyuhong Shim, Iksoo Choi, Wonyong Sung +1
While Transformer-based models have shown impressive language modeling performance, the large computation cost is often prohibitive for practical use. Attention head pruning, which…