5 citations · 7 across the 12 of their papers we have counts for
3 papers · 1 filter
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…
InfiniPot: Infinite Context Processing on Memory-Constrained LLMs
Minsoo Kim, Kyuhong Shim, Jungwook Choi +1
Handling long input contexts remains a significant challenge for Large Language Models (LLMs), particularly in resource-constrained environments such as mobile devices. Our work ai…
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…