SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs? (2025.acl-long)
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| Challenge: | Recent advances in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model’s utility for legitimate knowledge. |
| Approach: | They propose a Selected-Expert Unlearning Framework (SEUF) that combines expert attribution and an anchor loss to ensure controlled unlearning. |
| Outcome: | Experiments show that the proposed framework improves forget quality and model utility by 35% on MoE LLMs across benchmarks and LLM architectures compared to standard unlearning algorithms . |
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Do Domain-specific Experts exist in MoE-based LLMs? (2026.findings-acl)
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| Challenge: | Existing studies on domain-specific experts in Large Language Models (LLMs) are still lacking. |
| Approach: | They propose a training-free framework that introduces zero additional inference cost and outperforms well-trained MoE-based LLMs. |
| Outcome: | The proposed framework outperforms well-trained MoE-based LLMs and strong baselines across target and non-target domains. |
Faster MoE LLM Inference for Extremely Large Models (2026.findings-acl)
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| Challenge: | Existing inference optimizations for coarse-grained Mixture-of-Experts models implicitly assume a fixed activation budget, which is poorly understood. |
| Approach: | They propose a training-free policy that adapts token-level activation using router confidence and entropy while remaining within the model’s original budget. |
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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. |
| Approach: | They propose a mixture-of-experts architecture that allows for model scaling without sacrificing computational efficiency. |
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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. |
| Approach: | They propose an expert-specialized fine-tuning method that tunes the experts most relevant to downstream tasks while freezing the other experts. |
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Decoding Knowledge Attribution in Mixture-of-Experts: A Framework of Basic-Refinement Collaboration and Efficiency Analysis (2025.acl-long)
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| Challenge: | Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures. |
| Approach: | They propose to analyze sparse MoE architectures against dense models to capture dynamic routing-expert interactions. |
| Outcome: | The proposed algorithm shows that sparse models achieve higher efficiency per layer . it also shows that deep Qwen-MoE mitigates expert failures while minimizing complexity . |
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes. |
| Approach: | They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques. |
| Outcome: | The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks. |
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts (2025.acl-long)
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| Challenge: | Existing large language models (LLMs) have remarkable ability in high-resource languages, but their performance in multilingual scenarios is still limited. |
| Approach: | They propose a layer-wise expert allocation algorithm to determine the appropriate number of new experts for each layer. |
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HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts (2024.acl-long)
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| Challenge: | Existing methods for enhancing performance through increased use of expert knowledge often result in diminishing sparsity during expert selection. |
| Approach: | They propose a framework that integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. |
| Outcome: | The proposed framework outperforms existing methods under identical conditions concerning the number of experts. |
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference (2026.acl-long)
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| Challenge: | Existing approaches that reduce expert activations lead to severe model performance degradation. |
| Approach: | They propose a framework that optimizes budget allocation coordinately at layer and token levels to minimize model performance degradation. |
| Outcome: | The proposed framework achieves 1.15 prefill and 1.34 decode speedups on DeepSeek-V2-Lite at half of the original budget. |
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models (2025.acl-long)
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| Challenge: | Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs . however, it is hindered by two critical challenges: substantial GPU memory consumption and low activated parameters. |
| Approach: | They propose an Expert-Selection Aware Compressor for Mixture-of-Experts (MoE) that aligns with the characteristics of MoE from the perspectives of quantization and pruning. |
| Outcome: | The proposed approach significantly reduces memory usage and improves inference speed with minimal performance degradation. |