Papers by Junhyun Lee
Grouped Adaptive Weight Sharing (GAWS): An Inference-Efficient Adaptation Method for Large Language Models (2026.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) is a new approach to fine-tuning large language models . adapters are lightweight, task specific modules that can be used for adapters in latency-sensitive settings. |
| Approach: | They propose a low-rank adapter with a weight sharing mechanism that reduces latency by 40% . they analyze LoRA adapters on GPUs and identify segmented function calls as the primary source of latency. |
| Outcome: | The proposed adapter reduces latency to about 40% of the gap between the unmerged LoRA and the base model while maintaining parameter efficiency and comparable accuracy. |
zFLoRA: Zero-Latency Fused Low-Rank Adapters (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly deployed with task-specific adapters catering to multiple downstream applications. |
| Approach: | They propose a low-latency fused low-rank adapter that introduces zero latency overhead on top of the base model. |
| Outcome: | The proposed adapter reduces the inference time of the model by 2.5x . the proposed adapters are tested on 18 different tasks on different platforms . |