On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions

• URL: http://arxiv.org/abs/2410.02935v1
• Date: Thu, 3 Oct 2024 19:28:52 GMT
• Title: On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions
• Authors: Huy Nguyen, Xing Han, Carl William Harris, Suchi Saria, Nhat Ho,
• Abstract summary: Hierarchical Mixture of Experts (HMoE) excels at handling complex inputs and improving performance on targeted tasks. We theoretically demonstrate that applying tailored gating functions to each expert group allows HMoE to achieve robust results. This includes large-scale multimodal tasks, image classification, and latent domain discovery and prediction tasks, where our modified HMoE models show great performance improvements.
• Score: 29.130355774088205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing prominence of the Mixture of Experts (MoE) architecture in developing large-scale foundation models, we investigate the Hierarchical Mixture of Experts (HMoE), a specialized variant of MoE that excels in handling complex inputs and improving performance on targeted tasks. Our investigation highlights the advantages of using varied gating functions, moving beyond softmax gating within HMoE frameworks. We theoretically demonstrate that applying tailored gating functions to each expert group allows HMoE to achieve robust results, even when optimal gating functions are applied only at select hierarchical levels. Empirical validation across diverse scenarios supports these theoretical claims. This includes large-scale multimodal tasks, image classification, and latent domain discovery and prediction tasks, where our modified HMoE models show great performance improvements.

Related papers

• Retraining-Free Merging of Sparse Mixture-of-Experts via Hierarchical Clustering [14.858134039539697]
  We propose Hierarchical Clustering for Sparsely activated Mixture of Experts (HC-SMoE) HC-SMoE is a task-agnostic expert merging framework that reduces SMoE model parameters without retraining. We validate our approach through extensive experiments on eight zero-shot language tasks and demonstrate its effectiveness in large-scale SMoE models such as Qwen and Mixtral.
  arXiv  Detail & Related papers  (2024-10-11T07:36:14Z)
• Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging [36.0133566024214]
  Upcycling Instruction Tuning (UpIT) is a data-efficient approach for tuning a dense pre-trained model into a MoE instruction model. To ensure each specialized expert in the MoE model works as expected, we select a small amount of seed data that each expert excels to pre-optimize the router.
  arXiv  Detail & Related papers  (2024-10-02T14:48:22Z)
• Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts [75.85448576746373]
  We propose a method of grouping and pruning similar experts to improve the model's parameter efficiency. We validate the effectiveness of our method by pruning three state-of-the-art MoE architectures. The evaluation shows that our method outperforms other model pruning methods on a range of natural language tasks.
  arXiv  Detail & Related papers  (2024-07-12T17:25:02Z)
• Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models [24.915387910764082]
  Expert-Specialized Fine-Tuning, or ESFT, tunes the experts most relevant to downstream tasks while freezing the other experts and modules. MoE models with finer-grained experts are more advantageous in selecting the combination of experts that are most relevant to downstream tasks.
  arXiv  Detail & Related papers  (2024-07-02T03:11:13Z)
• Harder Tasks Need More Experts: Dynamic Routing in MoE Models [58.18526590138739]
  We introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models. Our method dynamically selects experts based on the confidence level in expert selection for each input.
  arXiv  Detail & Related papers  (2024-03-12T13:41:15Z)
• On Least Square Estimation in Softmax Gating Mixture of Experts [78.3687645289918]
  We investigate the performance of the least squares estimators (LSE) under a deterministic MoE model. We establish a condition called strong identifiability to characterize the convergence behavior of various types of expert functions. Our findings have important practical implications for expert selection.
  arXiv  Detail & Related papers  (2024-02-05T12:31:18Z)
• Inverse Reinforcement Learning with Sub-optimal Experts [56.553106680769474]
  We study the theoretical properties of the class of reward functions that are compatible with a given set of experts. Our results show that the presence of multiple sub-optimal experts can significantly shrink the set of compatible rewards. We analyze a uniform sampling algorithm that results in being minimax optimal whenever the sub-optimal experts' performance level is sufficiently close to the one of the optimal agent.
  arXiv  Detail & Related papers  (2024-01-08T12:39:25Z)
• Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts [74.40198929049959]
  Large multi-modal models (LMMs) exhibit remarkable performance across numerous tasks. generalist LMMs often suffer from performance degradation when tuned over a large collection of tasks. We propose Omni-SMoLA, an architecture that uses the Soft MoE approach to mix many multimodal low rank experts.
  arXiv  Detail & Related papers  (2023-12-01T23:04:27Z)
• MoEC: Mixture of Expert Clusters [93.63738535295866]
  Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE converts dense layers into sparse experts, and utilizes a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation.
  arXiv  Detail & Related papers  (2022-07-19T06:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.