Attention is a mechanism used in neural networks, particularly in the domain of Generative AI, to dynamically focus on specific parts of the input data when producing an output. Introduced in the context of sequence-to-sequence models, attention allows the model to weigh the importance of different input tokens, enabling it to capture dependencies and relationships across long sequences more effectively. This mechanism is a core component of transformer architectures, which underpin many state-of-the-art Large Language Models (LLMs). By assigning varying levels of importance to different parts of the input, attention enhances the model's ability to understand context and generate coherent and contextually relevant outputs.
The LLM Knowledge Base is a collection of bite-sized explanations for commonly used terms and abbreviations related to Large Language Models and Generative AI.
It's an educational resource that helps you stay up-to-date with the latest developments in AI research and its applications.