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    <abstract>The Mixture of Experts (MoE) architecture improves large language models (LLMs) by utilizing sparsely activated expert sub-networks with a routing module, but it typically demands high training cost. Previous work introduces parameter-efficient fine-tuning (PEFT) modules, e.g., LoRA, to achieve a lightweight MoE for training efficiency. However, they construct static experts by manually splitting the LoRA parameters into fixed groups, which limits flexibility and dynamism. Furthermore, this manual partitioning also hinders the effective utilization of well-initialized LoRA modules. To address the challenges, we first delve into the parameter patterns in LoRA modules, revealing that there exists task-relevant parameters that are concentrated along the rank dimension of the LoRA parameters. Based on this, we redesign the construction of experts and propose the method LoRACoE (LoRA Composition of Experts). Specifically, when confronted with a task, it dynamically builds experts based on rank-level parameter composition, i.e., experts can flexibly combine rank-level parameters in LoRA module. Extensive experiments demonstrate that compared to other LoRA-based MoE methods, our method achieves better task performance across a broader range of tasks.</abstract>
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%0 Conference Proceedings
%T LoRACoE: Improving Large Language Model via Composition-based LoRA Expert
%A Li, Guanyu
%A Xi, Zhiheng
%A Zhang, Zhihao
%A Hong, Boyang
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-loracoe
%X The Mixture of Experts (MoE) architecture improves large language models (LLMs) by utilizing sparsely activated expert sub-networks with a routing module, but it typically demands high training cost. Previous work introduces parameter-efficient fine-tuning (PEFT) modules, e.g., LoRA, to achieve a lightweight MoE for training efficiency. However, they construct static experts by manually splitting the LoRA parameters into fixed groups, which limits flexibility and dynamism. Furthermore, this manual partitioning also hinders the effective utilization of well-initialized LoRA modules. To address the challenges, we first delve into the parameter patterns in LoRA modules, revealing that there exists task-relevant parameters that are concentrated along the rank dimension of the LoRA parameters. Based on this, we redesign the construction of experts and propose the method LoRACoE (LoRA Composition of Experts). Specifically, when confronted with a task, it dynamically builds experts based on rank-level parameter composition, i.e., experts can flexibly combine rank-level parameters in LoRA module. Extensive experiments demonstrate that compared to other LoRA-based MoE methods, our method achieves better task performance across a broader range of tasks.
%U https://aclanthology.org/2025.emnlp-main.1594/
%P 31278-31292

Markdown (Informal)

[LoRACoE: Improving Large Language Model via Composition-based LoRA Expert](https://aclanthology.org/2025.emnlp-main.1594/) (Li et al., EMNLP 2025)

• LoRACoE: Improving Large Language Model via Composition-based LoRA Expert (Li et al., EMNLP 2025)

ACL

• Guanyu Li, Zhiheng Xi, Zhihao Zhang, Boyang Hong, Tao Gui, Qi Zhang, and Xuanjing Huang. 2025. LoRACoE: Improving Large Language Model via Composition-based LoRA Expert. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 31278–31292, Suzhou, China. Association for Computational Linguistics.