AoE lets experts in Mixture-of-Experts models self-select based on internal activations, improving efficiency and performance. This addresses the issue of router-expert separation, which can lead to suboptimal expert selection.
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https://arxiv.org/abs/2501.13074
Original Problem 🤔:
→ Mixture-of-Experts (MoE) models use a router to assign tokens to expert modules.
→ This separation can lead to suboptimal expert selection and inefficient learning.
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Solution in this Paper 💡:
→ This paper proposes Autonomy-of-Experts (AoE).
→ In AoE, experts self-select based on their internal activation scale.
→ Routers are removed.
→ Experts pre-compute internal activations and are ranked by their norms.
→ Only top-ranking experts process the input.
→ A low-rank weight factorization reduces overhead.
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Key Insights from this Paper 🔑:
→ Experts are aware of their ability to handle inputs, as reflected in internal activations.
→ Self-evaluation by experts leads to better expert selection and more effective learning.
→ AoE simplifies MoE training by removing the need for auxiliary load balancing loss.
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Results 💯:
→ AoE outperforms traditional MoE on downstream tasks with comparable efficiency, sometimes needing no auxiliary load balancing loss.
→ AoE achieves up to 97% throughput of MoE, though with higher memory usage.
→ AoE shows improved load balancing and higher confidence in expert selection.
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1ST SET OF HOOKS
Experts choose their own tasks: Autonomy-of-Experts for better LLM efficiency and performance.
AoE: Giving LLMs expert autonomy for superior performance.
LLM experts get self-aware: AoE boosts efficiency and effectiveness.
Empowering LLM experts with self-selection: The AoE advantage.
2nd SET OF HOOKS
LLM experts know best: AoE cuts the middleman for better performance.
Self-driving experts in LLMs: AoE for a smarter MoE.
No more bossy routers: AoE lets LLM experts choose their own work.
Trust the experts: AoE for efficient and effective LLM training.
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