"Attention", "Transformers", in Neural Network "Large Language Models"

Dec 24, 2023 ·

The article discusses Neural Networks (NNs) and their application to the use of attention-based models and Transformers. NNs are powerful algorithms used in machine learning, which allow machines to learn from data. They are capable of performing complex tasks, such as image recognition or natural language processing. Attention-based models are a type of NN that uses self-attention layers to focus on specific parts of an input sequence. This allows them to better understand context and relationships within the input data. Transformers are a type of NN that use self-attention layers to enable parallelization across multiple tasks. These models can be used for tasks such as text translation, question answering, and natural language understanding. The article then provides a high-level overview of the Transformer architecture and compares it to other types of NNs. It also discusses the importance of attention in transformer-based models and its implications for machine learning. Finally, the article examines the applications of these models in natural language processing, and offers some best practices for using them. In conclusion, attention-based models and Transformers are powerful tools that can be used for various tasks in machine learning, and they have become increasingly important in natural language processing.