Higher Layers Need More LoRA Experts

TL;DR

This paper introduces MoLA, a layer-wise expert allocation method for LoRA in Transformer models, which improves performance and efficiency by assigning more experts to higher layers, demonstrating superior results on NLP benchmarks.

Contribution

The paper proposes a novel layer-wise expert allocation strategy for LoRA in MoE models, enhancing efficiency and performance in parameter-efficient tuning.

Findings

Allocating more LoRA experts to higher layers improves model performance.

MoLA outperforms baselines with fewer parameters.

Layer-wise expert configuration is effective across NLP benchmarks.

Abstract

Parameter-efficient tuning (PEFT) techniques like low-rank adaptation (LoRA) offer training efficiency on Large Language Models, but their impact on model performance remains limited. Recent efforts integrate LoRA and Mixture-of-Experts (MoE) to improve the performance of PEFT methods. Despite promising results, research on improving the efficiency of LoRA with MoE is still in its early stages. Recent studies have shown that experts in the MoE architecture have different strengths and also exhibit some redundancy. Does this statement also apply to parameter-efficient MoE? In this paper, we introduce a novel parameter-efficient MoE method, \textit{\textbf{M}oE-L\textbf{o}RA with \textbf{L}ayer-wise Expert \textbf{A}llocation (MoLA)} for Transformer-based models, where each model layer has the flexibility to employ a varying number of LoRA experts. We investigate several architectures with varying layer-wise expert configurations. Experiments on six well-known NLP and commonsense QA benchmarks demonstrate that MoLA achieves equal or superior performance compared to all baselines. We find that allocating more LoRA experts to higher layers further enhances the effectiveness of models with a certain number of experts in total. With much fewer parameters, this allocation strategy outperforms the setting with the same number of experts in every layer. This work can be widely used as a plug-and-play parameter-efficient tuning approach for various applications. The code is available at https://github.com/GCYZSL/MoLA.