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About this paper
Sejik Park
An algorithm is introduced to combine frequently selected experts within Mixture-of-Experts models, aiming to generate more generalized features. This approach yields modest improvements in transfer and average accuracy on multi-domain task incremental learning benchmarks, while reducing expert redundancy and maintaining plasticity.
Key insights
- Performance degradation in MoE with shared layers may stem from the redundant learning of similar features by different experts.
- Merging frequently used experts can foster more general features by combining learned representations, improving knowledge transfer across tasks.
- Dynamically managing experts through merging can lead to more efficient utilization of model resources and potentially fewer fixed experts over time.
Summary by alphaXiv.
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