Summary of Scaling Laws Across Model Architectures: a Comparative Analysis Of Dense and Moe Models in Large Language Models, by Siqi Wang et al.
Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models
by Siqi Wang, Zhengyu Chen, Bei Li, Keqing He, Min Zhang, Jingang Wang
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the scaling laws of large language models (LLMs) to improve their efficiency and effectiveness. It focuses on two types of LLMs: Dense Models and Mixture of Experts (MoE) Models. The authors explore the transferability and discrepancies between these two architectures, analyzing theoretical aspects and conducting extensive experiments. They find that the power-law scaling framework applies to MoE Models as well, indicating preserved fundamental principles despite architectural differences. Furthermore, they observe superior generalization capabilities in MoE Models, achieving lower testing losses with the same training budget compared to Dense Models. These findings have implications for optimizing MoE Model training and deployment strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models grow and become more efficient. It compares two types of these models: ones that use dense connections and others that use a mixture of experts. The researchers examine why the scaling laws might be different or the same between these two types, using both theory and lots of experiments. They discover that the rules for scaling large language models apply equally to both types, even though they are built differently. Additionally, they find that the expert-mixing approach leads to better results when tested on new data, while still needing the same amount of training. This has important implications for how these models can be trained and used in real-world applications. |
Keywords
» Artificial intelligence » Generalization » Mixture of experts » Scaling laws » Transferability
