Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations
Fuente:
arXiv
Saved in:
| Main Authors: | Dong, Zican, Peng, Han, Liu, Peiyu, Zhao, Wayne Xin, Wu, Dong, Xiao, Feng, Wang, Zhifeng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
by: Dong, Zican, et al.
Published: (2026)
by: Dong, Zican, et al.
Published: (2026)
How Efficient Are Diffusion Language Models? A Critical Examination of Efficiency Evaluation Practices
by: Peng, Han, et al.
Published: (2025)
by: Peng, Han, et al.
Published: (2025)
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation
by: Dong, Zican, et al.
Published: (2025)
by: Dong, Zican, et al.
Published: (2025)
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
by: Dong, Zican, et al.
Published: (2024)
by: Dong, Zican, et al.
Published: (2024)
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)
by: Lu, Xudong, et al.
Published: (2024)
A Survey on Long Text Modeling with Transformers
by: Dong, Zican, et al.
Published: (2023)
by: Dong, Zican, et al.
Published: (2023)
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability
by: Peng, Han, et al.
Published: (2025)
by: Peng, Han, et al.
Published: (2025)
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
by: Dong, Zican, et al.
Published: (2023)
by: Dong, Zican, et al.
Published: (2023)
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
by: Xie, Yanyue, et al.
Published: (2024)
by: Xie, Yanyue, et al.
Published: (2024)
Pruning and Distilling Mixture-of-Experts into Dense Language Models
by: Kim, Junhyuck, et al.
Published: (2026)
by: Kim, Junhyuck, et al.
Published: (2026)
Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)
by: Wu, Wenhao, et al.
Published: (2025)
ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning
by: Gao, Shangqian, et al.
Published: (2025)
by: Gao, Shangqian, et al.
Published: (2025)
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
by: Zhao, Zhongyu, et al.
Published: (2024)
by: Zhao, Zhongyu, et al.
Published: (2024)
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
by: Li, Guanchen, et al.
Published: (2024)
by: Li, Guanchen, et al.
Published: (2024)
MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models
by: Liu, Zehua, et al.
Published: (2025)
by: Liu, Zehua, et al.
Published: (2025)
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
by: Qin, Zhen, et al.
Published: (2024)
by: Qin, Zhen, et al.
Published: (2024)
Bayesian Mixture of Experts For Large Language Models
by: Dialameh, Maryam, et al.
Published: (2025)
by: Dialameh, Maryam, et al.
Published: (2025)
On the Utility of Domain-Adjacent Fine-Tuned Model Ensembles for Few-shot Problems
by: Alam, Md Ibrahim Ibne, et al.
Published: (2024)
by: Alam, Md Ibrahim Ibne, et al.
Published: (2024)
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
by: Song, Chenyang, et al.
Published: (2026)
by: Song, Chenyang, et al.
Published: (2026)
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning
by: Sarkar, Soumajyoti, et al.
Published: (2024)
by: Sarkar, Soumajyoti, et al.
Published: (2024)
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
by: Guo, Hongcheng, et al.
Published: (2025)
by: Guo, Hongcheng, et al.
Published: (2025)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints
by: Liew, Seng Pei, et al.
Published: (2026)
by: Liew, Seng Pei, et al.
Published: (2026)
Upcycling Large Language Models into Mixture of Experts
by: He, Ethan, et al.
Published: (2024)
by: He, Ethan, et al.
Published: (2024)
Maximum Score Routing For Mixture-of-Experts
by: Dong, Bowen, et al.
Published: (2025)
by: Dong, Bowen, et al.
Published: (2025)
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
by: Liu, Xianggen, et al.
Published: (2024)
by: Liu, Xianggen, et al.
Published: (2024)
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
by: Cheng, Xiang, et al.
Published: (2025)
by: Cheng, Xiang, et al.
Published: (2025)
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
by: Wang, Zihan, et al.
Published: (2025)
by: Wang, Zihan, et al.
Published: (2025)
A Survey on Mixture of Experts in Large Language Models
by: Cai, Weilin, et al.
Published: (2024)
by: Cai, Weilin, et al.
Published: (2024)
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts
by: Wang, Haoxiang, et al.
Published: (2024)
by: Wang, Haoxiang, et al.
Published: (2024)
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts
by: Zhang, Di, et al.
Published: (2025)
by: Zhang, Di, et al.
Published: (2025)
Mixture of Lookup Experts
by: Jie, Shibo, et al.
Published: (2025)
by: Jie, Shibo, et al.
Published: (2025)
A Closer Look into Mixture-of-Experts in Large Language Models
by: Lo, Ka Man, et al.
Published: (2024)
by: Lo, Ka Man, et al.
Published: (2024)
HMoE: Heterogeneous Mixture of Experts for Language Modeling
by: Wang, An, et al.
Published: (2024)
by: Wang, An, et al.
Published: (2024)
Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)
by: Liu, Yilun, et al.
Published: (2026)
EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning
by: Zhao, Songlin, et al.
Published: (2025)
by: Zhao, Songlin, et al.
Published: (2025)
Probing Semantic Routing in Large Mixture-of-Expert Models
by: Olson, Matthew Lyle, et al.
Published: (2025)
by: Olson, Matthew Lyle, et al.
Published: (2025)
GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models
by: Zheng, Chen, et al.
Published: (2025)
by: Zheng, Chen, et al.
Published: (2025)
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity
by: Song, Chenyang, et al.
Published: (2025)
by: Song, Chenyang, et al.
Published: (2025)
Similar Items
-
ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution
by: Dong, Zican, et al.
Published: (2026) -
How Efficient Are Diffusion Language Models? A Critical Examination of Efficiency Evaluation Practices
by: Peng, Han, et al.
Published: (2025) -
LongReD: Mitigating Short-Text Degradation of Long-Context Large Language Models via Restoration Distillation
by: Dong, Zican, et al.
Published: (2025) -
Exploring Context Window of Large Language Models via Decomposed Positional Vectors
by: Dong, Zican, et al.
Published: (2024) -
Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
by: Lu, Xudong, et al.
Published: (2024)