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Zequan Liu

2 accepted papers

2024

ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models

NAACL 2024long

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 commendable performance as a popular and representative method. However, it is implemented with a fixed intrinsic rank that m…

2024

PARA: Parameter-Efficient Fine-tuning with Prompt-Aware Representation Adjustment

EMNLP 2024industry

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 in both efficiency and performance within the context of single-backbone multi-tenant applications. This paper introduce…