| Challenge: | Existing MNNs are explicit, with predefined modular architectures and individual modules expected to implement distinct functions. |
| Approach: | They propose to unlock emergent modularity in language models by fine-tuning them as Mixture-of-Experts (MoEs) EMoE is robust to various configurations and can scale up to Large Language Models . |
| Outcome: | The proposed models can be fine-tuned as Mixture-of-Expert (MoE) counterparts without introducing any extra parameters. |
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| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
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MMNMT: Modularizing Multilingual Neural Machine Translation with Flexibly Assembled MoE and Dense Blocks (2023.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) based sparse architectures are prone to overfitting on low-resource language translation. |
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| Challenge: | Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. |
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| Challenge: | Large language models excel at understanding and generating human-like text, but their widespread deployment can be prohibitively expensive. |
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A Closer Look into Mixture-of-Experts in Large Language Models (2025.findings-naacl)
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| Challenge: | Mixture-of-experts (MoE) architectures are gaining increasing attention for their unique properties and remarkable performance. |
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ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings (2026.findings-acl)
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Breaking ReLU Barrier: Generalized MoEfication for Dense Pretrained Models (2024.emnlp-main)
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MoExtend: Tuning New Experts for Modality and Task Extension (2024.acl-srw)
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| Challenge: | Existing instruction tuning methods for large language models (LLMs) are costly and difficult to implement. |
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