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September 23, 2025Open Access

CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning

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Novel contrastive representation improves modularization and specialization in mixture-of-experts models, enhancing performance.

CoMoE enhances the capacity of mixture-of-experts, promoting better specialization among experts in model training.
Experiments demonstrated improvements across various benchmarks, suggesting enhanced performance in heterogeneous datasets.
The method uses a contrastive objective that recovers information gaps between activated and inactivated experts.
The study emphasizes the importance of effective module training for optimal use of expert capacities.

Feng et al. (2025) studied this question.

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September 23, 2025Open Access

View Full Paper

Ask AI

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Authors

Novel contrastive representation improves modularization and specialization in mixture-of-experts models, enhancing performance.

CoMoE enhances the capacity of mixture-of-experts, promoting better specialization among experts in model training.
Experiments demonstrated improvements across various benchmarks, suggesting enhanced performance in heterogeneous datasets.
The method uses a contrastive objective that recovers information gaps between activated and inactivated experts.
The study emphasizes the importance of effective module training for optimal use of expert capacities.

Feng et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark