2 citations · 4 across the 6 of their papers we have counts for
6 papers
RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy
Geonho Lee, Janghwan Lee, Sukjin Hong +4
Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful too…
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference
Janghwan Lee, Jiwoong Park, Jinseok Kim +4
As large language models (LLMs) grow in parameter size and context length, computation precision has been reduced from 16-bit to 4-bit to improve inference efficiency. However, thi…
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment
Janghwan Lee, Seongmin Park, Sukjin Hong +3
The rapid advancement of large language models (LLMs) has facilitated their transformation into conversational chatbots that can grasp contextual nuances and generate pertinent sen…
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…
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
Minjae Lee, Seongmin Park, Hyungmin Kim +6
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and laten…