Higher Layers Need More LoRA Experts

• URL: http://arxiv.org/abs/2402.08562v1
• Date: Tue, 13 Feb 2024 16:04:21 GMT
• Title: Higher Layers Need More LoRA Experts
• Authors: Chongyang Gao and Kezhen Chen and Jinmeng Rao and Baochen Sun and Ruibo Liu and Daiyi Peng and Yawen Zhang and Xiaoyuan Guo and Jie Yang and VS Subrahmanian
• Abstract summary: We introduce a novel parameter-efficient MoE method, textittextbfMoE-LtextbfoRA with textbfLayer-wise Expert textbfAllocation (MoLA) for Transformer-based models. Experiments on six well-known NLP and commonsense QA benchmarks demonstrate that MoLA achieves equal or superior performance compared to all baselines.
• Score: 23.72297945365351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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