AI Research
Depth-Aware Sensitivity Analysis of Mixture-of-Experts Models via Magnitude-Based Expert Masking
Unlocking efficient model compression through depth-dependent expert pruning in MoE architectures
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) while preserving computational efficiency through sparse activation. Despite their widespread adoption, the relative importance of individual MoE layers remains insufficiently characterized, particularly fo...
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ArXiv CS.AI
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ArXiv CS.AI
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This research reveals that Mixture-of-Experts models are not uniform, with late-stage layers offering significant redundancy for aggressive pruning. By shifting focus toward depth-aware masking, developers can drastically shrink model footprints without sacrificing performance on complex tasks like code translation.
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