This paper introduces a novel approach to language modeling using gated convolutional networks (GCNNs). Unlike traditional recurrent neural networks (RNNs), GCNNs use stacked convolutions to capture long-term dependencies more efficiently and with parallelization. The authors propose a simplified gating mechanism, called Gated Linear Units (GLUs), which outperforms existing methods and reduces the vanishing gradient problem. The proposed model achieves state-of-the-art performance on the WikiText-103 benchmark and competitive results on the Google Billion Words benchmark, demonstrating its effectiveness in handling large-scale language tasks. Additionally, the GCNN model significantly reduces latency compared to RNNs, making it more efficient for real-time applications. The paper also explores the impact of architectural choices and compares different gating mechanisms, showing that GLUs provide better convergence and accuracy. Overall, the GCNN approach offers a promising alternative to RNNs for language modeling, particularly in terms of computational efficiency and performance.This paper introduces a novel approach to language modeling using gated convolutional networks (GCNNs). Unlike traditional recurrent neural networks (RNNs), GCNNs use stacked convolutions to capture long-term dependencies more efficiently and with parallelization. The authors propose a simplified gating mechanism, called Gated Linear Units (GLUs), which outperforms existing methods and reduces the vanishing gradient problem. The proposed model achieves state-of-the-art performance on the WikiText-103 benchmark and competitive results on the Google Billion Words benchmark, demonstrating its effectiveness in handling large-scale language tasks. Additionally, the GCNN model significantly reduces latency compared to RNNs, making it more efficient for real-time applications. The paper also explores the impact of architectural choices and compares different gating mechanisms, showing that GLUs provide better convergence and accuracy. Overall, the GCNN approach offers a promising alternative to RNNs for language modeling, particularly in terms of computational efficiency and performance.