Optimal Sparsity in Mixture-of-Experts Language Models: Balancing Parameters and FLOPs

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Efficiency Gains Through Sparsity: New Insights into Scaling Laws for Mixture-of-Experts Language Models
Scaling language models has proven to be key to improving performance and unlocking new applications. Two main factors determine a model's capacity: the number of parameters and the computational cost per example (FLOPs). Traditionally, both factors are increased in parallel, but the exact interplay and their respective contributions to overall capacity are not yet fully understood. Recent research investigates this relationship in the context of Sparse Mixture-of-Experts (MoE) models. This architecture allows scaling the number of parameters without proportionally increasing FLOPs.
MoE models are characterized by their specific structure. They consist of multiple expert networks, each specializing in specific sub-domains of the input space. A so-called gating network decides which experts are relevant for processing a particular input. By selectively activating only a few experts, the computational cost is reduced compared to a dense model with the same number of parameters. Sparsity, i.e., the proportion of inactive parameters, plays a crucial role in this.
The study investigates the influence of different sparsity levels on the performance of MoE models during pre-training and subsequent few-shot evaluation. Different scenarios with varying constraints on parameter size and total computational cost were considered. The results show that under various conditions, there is an optimal sparsity level that improves both training efficiency and model performance.
The researchers found that too little sparsity leads to increased model complexity and computational cost without a proportional increase in performance. Conversely, too much sparsity can limit model capacity and impair performance, as potentially not enough experts are activated to adequately process the input. The optimal sparsity level therefore lies in the middle ground and must be determined individually for each model and task.
These findings contribute to a better understanding of the scaling laws for MoE models and complement existing work in this area. They offer valuable insights for designing more efficient architectures and allow for optimizing the ratio of parameter count and computational cost. Developing powerful language models while simultaneously reducing resource requirements is a central concern of AI research. The results of this study show that sparsity is a promising approach to achieve this goal.
For companies like Mindverse, which specialize in the development of AI solutions, these findings are particularly relevant. Optimizing language models for efficiency and scalability is crucial for their deployment in real-world applications such as chatbots, voicebots, AI search engines, and knowledge bases. The research results offer new starting points for the development of customized AI solutions that optimally meet customer requirements. By integrating sparsity concepts, more resource-efficient and powerful models can be developed, creating added value for businesses and users.
Bibliographie: https://arxiv.org/abs/2501.12370 https://arxiv.org/html/2501.12370v1 https://www.researchgate.net/publication/388317644_Parameters_vs_FLOPs_Scaling_Laws_for_Optimal_Sparsity_for_Mixture-of-Experts_Language_Models https://chatpaper.com/chatpaper/paper/101230 https://arxiv-sanity-lite.com/?rank=pid&pid=2501.12370 https://www.alphaxiv.org/abs/2501.12370 https://www.chatpaper.com/chatpaper/zh-CN/paper/101230 https://openreview.net/pdf?id=i9K2ZWkYIP https://aclanthology.org/2024.emnlp-main.319.pdf https://openreview.net/pdf?id=Iizr8qwH7J