1 citations · 1 across the 3 of their papers we have counts for
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ShapLoRA: Allocation of Low-rank Adaption on Large Language Models via Shapley Value Inspired Importance Estimation
Yi Zhao, Qinghua Yao, Xinyuan song +1
Low-rank adaption (LoRA) is a representative method in the field of parameter-efficient fine-tuning (PEFT), and is key to Democratizating the modern large language models (LLMs). T…
PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment
Zequan Liu, Yi Zhao, Ming Tan +2
In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels…
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…
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification
Tianfang Xie, Tianjing Li, Wei Zhu +2
Due to their substantial sizes, large language models (LLMs) are typically deployed within a single-backbone multi-tenant framework. In this setup, a single instance of an LLM back…