  Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring
pre-trained large language models (LLMs), especially as the models' scale and
the diversity of tasks increase. Low-rank adaptation (LoRA) is based on the
idea that the adaptation process is intrinsically low-dimensional, i.e.,
significant model changes can be represented with relatively few parameters.
However, decreasing the rank encounters challenges with generalization errors
for specific tasks when compared to full-parameter fine-tuning. We present
MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters
while maintaining a higher rank, thereby offering improved performance
potential. The core idea is to freeze original pretrained weights and train a
group of mini LoRAs with only a small number of parameters. This can capture a
significant degree of diversity among mini LoRAs, thus promoting better
generalization ability. We conduct a theoretical analysis and empirical studies
on various NLP tasks. Our experimental results show that, compared to LoRA,
MELoRA achieves better performance with 8 times fewer trainable parameters on
natural language understanding tasks and 36 times fewer trainable parameters on
instruction following tasks, which demonstrates the effectiveness of MELoRA.
