Cosine-similarity routing to semantic anchors in MoE models matches linear routing perplexity on WikiText-103 while providing direct traceability of decisions and a bandpass loss that cuts dead experts from 30-45% to 0-6%.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence
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Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Models
Cosine-similarity routing to semantic anchors in MoE models matches linear routing perplexity on WikiText-103 while providing direct traceability of decisions and a bandpass loss that cuts dead experts from 30-45% to 0-6%.
- Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It