MoELoRA: Contrastive Learning Guided Mixture of Experts on
  Parameter-Efficient Fine-Tuning for Large Language Models
Tongxu Luo Jiahe Lei Fangyu Lei Weihao Liu Shizhu He Jun Zhao Kang Liu
Fine-tuning is often necessary to enhance the adaptability of Large Language
Models (LLM) to downstream tasks. Nonetheless, the process of updating billions
of parameters demands significant computational resources and training time,
which poses a substantial obstacle to the widespread application of large-scale
models in various scenarios. To address this issue, Parameter-Efficient
Fine-Tuning (PEFT) has emerged as a prominent paradigm in recent research.
However, current PEFT approaches that employ a limited set of global parameters
(such as LoRA, which adds low-rank approximation matrices to all weights) face
challenges in flexibly combining different computational modules in downstream
tasks. In this work, we introduce a novel PEFT method: MoELoRA. We consider
LoRA as Mixture of Experts (MoE), and to mitigate the random routing phenomenon
observed in MoE, we propose the utilization of contrastive learning to
encourage experts to learn distinct features. We conducted experiments on 11
tasks in math reasoning and common-sense reasoning benchmarks. With the same
number of parameters, our approach outperforms LoRA significantly. In math
reasoning, MoELoRA achieved an average performance that was 4.2% higher than
LoRA, and demonstrated competitive performance compared to the 175B GPT-3.5 on
several benchmarks.