The comparative analysis shows that the proposed adaptive loss-driven gate module significantly enhances the learning process by effectively distributing gate values across expert models, outperforming traditional methods.
By removing the adaptive weight module, the LMoE still displays substantially higher utilities for all user types compared to MultVAE and MoE, highlighting its integral role in optimizing performance.
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