3 citations · 4 across the 2 of their papers we have counts for
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cs.CL2024★ 1 cited
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning
Jingfan Zhang, Yi Zhao, Dan Chen +3
Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latenc…
cs.CL2024★ 1 cited
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
Zequan Liu, Jiawen Lyn, Wei Zhu +2
Parameter-efficient fine-tuning (PEFT) is widely studied for its effectiveness and efficiency in the era of large language models. Low-rank adaptation (LoRA) has demonstrated comme…
cs.CL2024★ 3 cited
Text2MDT: Extracting Medical Decision Trees from Medical Texts
Wei Zhu, Wenfeng Li, Xing Tian +6
Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support systems. However, the current MDT c…