Mixture-of-Experts Architectures Advance

The field of Mixture-of-Experts (MoE) architectures is witnessing significant advancements, with a focus on improving efficiency, scalability, and adaptability. Researchers are exploring innovative approaches to transfer expertise from multiple task-specific models into a single compact model, enabling rapid adaptation to new tasks with minimal additions and tuning. The development of dynamic MoE approaches is also underway, aiming to evaluate their effectiveness in continual and reinforcement learning environments. Furthermore, the modularity of MoE architectures introduces unique challenges, such as the vulnerability to unauthorized compression and the need for secure specialization. Noteworthy papers in this area include:

  • Generalizable and Efficient Automated Scoring with a Knowledge-Distilled Multi-Task Mixture-of-Experts, which proposes a knowledge-distilled multi-task MoE approach for automated scoring.
  • Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models, which introduces a structured cluster-then-select process for generalizable pruning of MoE models.

Sources

Generalizable and Efficient Automated Scoring with a Knowledge-Distilled Multi-Task Mixture-of-Experts

Dynamic Mixture of Experts Against Severe Distribution Shifts

Exploiting the Experts: Unauthorized Compression in MoE-LLMs

Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models

MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training

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