| Challenge: | Large Language Models (LLMs) are characterized by their immense size, often consisting of at least one billion parameters. |
| Approach: | They propose a mixture of Frozen Experts architecture that integrates PEFT and MoE to enhance both training efficiency and model scalability. |
| Outcome: | The proposed architecture outperforms other methods while achieving the highest efficiency. |
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| Challenge: | Existing Parameter-Efficient Fine-Tuning (PEFT) strategies that focus on specialized experts are not effective for Mixture-of-Experts (MoE). |
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MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)
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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang
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Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024.emnlp-main)
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| Challenge: | Existing studies on parameter-efficient fine-tuning (PEFT) for dense-architecture LLMs are lacking. |
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| Challenge: | Existing benchmarks focus on isolated aspects of MoE, with conflicting conclusions . a lack of consensus on optimal design choices is limiting to specific aspects of the model. |
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MoLA: MoE LoRA with Layer-wise Expert Allocation (2025.findings-naacl)
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Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models (2022.coling-1)
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| Challenge: | Recent results show that the mix-of-experts architecture is parameter inefficient . large-scale pre-trained language models can achieve excellent performance in many NLP tasks. |
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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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LoRACoE: Improving Large Language Model via Composition-based LoRA Expert (2025.emnlp-main)
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| Challenge: | Recent studies show that the Mixture of Experts architecture improves performance of large language models. |
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