GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Summary

The paper introduces GLaM (Generalist Language Model), a large language model that utilizes a Mixture-of-Experts (MoE) architecture to achieve efficient scaling. GLaM demonstrates that it is possible to scale language models significantly by employing a sparse MoE approach, allowing the model to have a large number of parameters without a proportional increase in computational cost during inference. The paper details the architecture, training methodology, and evaluation results of GLaM, highlighting its performance on various natural language tasks, often surpassing previous state-of-the-art results. It examines the trade-offs between model size, training data, and computational resources, providing insights into the efficient design and scaling of large language models. The study shows that GLaM can achieve superior performance compared to dense models of similar size, while requiring lower computational resources during inference. This efficiency stems from the MoE design where only a small subset of the model's parameters are activated for each input token.

Key Takeaways

1. GLaM introduces a sparse Mixture-of-Experts architecture for large language models, enabling efficient scaling.
2. The MoE architecture allows for a massive parameter count while mitigating inference cost through sparse activation.
3. GLaM demonstrates superior performance on a variety of natural language tasks compared to similarly sized dense models.
4. The paper highlights the efficiency gains of MoE models, particularly in terms of inference speed and computational resource utilization.
5. The research suggests a shift towards sparse models to achieve better scaling of language models.