This 2023 paper Gaussian Error Linear Units (GELUs), a novel activation function for neural networks that outperforms traditional activations like Rectified Linear Units (ReLUs) and Exponential Linear Units (ELUs) across various tasks. GELUs operate by weighting inputs by their value using the standard Gaussian cumulative distribution function, providing a probabilistic interpretation unlike the sign-based gating of ReLUs. Empirical evaluations demonstrate consistent performance improvements in computer vision, natural language processing, and speech recognition tasks. The paper also discusses the historical context and challenges of credit assignment for a related activation function, the Sigmoid Linear Unit (SiLU), which was independently rediscovered and mislabeled as "swish" by other research groups. Ultimately, GELUs have gained prominence as a default activation in advanced Transformer models, indicating their significant impact on deep learning.