Unlocking Emergent Modularity in Large Language Models

• URL: http://arxiv.org/abs/2310.10908v2
• Date: Mon, 1 Apr 2024 11:37:39 GMT
• Title: Unlocking Emergent Modularity in Large Language Models
• Authors: Zihan Qiu, Zeyu Huang, Jie Fu,
• Abstract summary: We show that standard Language Models (LMs) could be fine-tuned as their Mixture-of-Expert (MoEs) counterparts without introducing any extra parameters. Our experiments demonstrate that fine-tuning EMoE effectively improves downstream in-domain and out-of-domain generalization compared with vanilla fine-tuning.
• Score: 27.12431620957652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modular Neural Networks (MNNs) demonstrate various advantages over monolithic models. Existing MNNs are generally $\textit{explicit}$: their modular architectures are pre-defined, with individual modules expected to implement distinct functions. Recent works reveal that there exists $\textit{implicit}$ modularity in standard pre-trained transformers, namely $\textit{Emergent Modularity}$. They indicate that such modular structures spontaneously exhibit during the early pre-training phase. Despite the benefits of modularity, most Language Models (LMs) are still treated as monolithic models in the pre-train and fine-tune paradigm, with their emergent modularity locked and underutilized. In this work, focusing on unlocking the emergent modularity in LMs, we showcase that standard LMs could be fine-tuned as their Mixture-of-Expert (MoEs) counterparts without introducing any extra parameters. Such MoEs are derived from emergent modularity and are referred to as Emergent MoEs (EMoE). Our experiments demonstrate that fine-tuning EMoE effectively improves downstream in-domain and out-of-domain generalization compared with vanilla fine-tuning. Our analysis and ablation studies further illustrate that it is robust to various configurations and can scale up to Large Language Models (i.e., Llama2-7B and Llama-30B). Code is available at https://github.com/qiuzh20/EMoE.

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