Papers by Eman Alsuradi
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. |