Summary of Relu’s Revival: on the Entropic Overload in Normalization-free Large Language Models, by Nandan Kumar Jha and Brandon Reagen
ReLU’s Revival: On the Entropic Overload in Normalization-Free Large Language Models
by Nandan Kumar Jha, Brandon Reagen
First submitted to arxiv on: 12 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 LayerNorm is a crucial component in large language models (LLMs) for stabilizing training and ensuring smooth optimization. However, it presents challenges in mechanistic interpretability, outlier feature suppression, faithful signal propagation, and computational complexity of private inference. This work investigates desirable activation functions in normalization-free decoder-only LLMs. Contrary to the conventional preference for GELU, our findings show that ReLU significantly outperforms GELU in LayerNorm-free models, resulting in a 8.2% perplexity improvement. We identify an issue with GELU, where early layers experience entropic overload, leading to under-utilization of attention heads’ representational capacity. This highlights GELU’s unsuitability for LayerNorm-free architectures, whereas ReLU’s geometrical properties lead to improved learning dynamics and better information retention in the absence of LayerNorm. This study provides key insights for optimizing transformer architectures where LayerNorm introduces challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to improve large language models that don’t use LayerNorm. LayerNorm is important because it helps training and optimization, but it can be tricky to understand and works poorly with certain types of data. The researchers found that using a different type of activation function called ReLU makes the model work much better, with an 8.2% improvement in how well it predicts language. They also discovered that GELU, another common activation function, has some problems when used without LayerNorm. This study can help people who are working on improving language models. |
Keywords
» Artificial intelligence » Attention » Decoder » Inference » Optimization » Perplexity » Relu » Transformer
