Multi-headed attention is a core component of the transformer architecture, widely used in natural language processing and other AI tasks. This mechanism involves dividing the model's attention into multiple heads, each of which processes the input data independently across different subsets of dimensions, allowing the model to capture various aspects of the data simultaneously. This parallel processing enhances the model's ability to learn complex patterns and relationships within the data, leading to better performance on tasks like translation, text summarization, and contextual understanding. The outputs of these multiple heads are then combined to form a single attention output, providing a comprehensive representation of the input features.
Historical overview: The concept of multi-headed attention was introduced with the transformer model in the seminal paper "Attention is All You Need" by Vaswani et al. in 2017. It quickly became a cornerstone technique in modern AI architectures due to its effectiveness and efficiency in handling sequences.
Key contributors: Ashish Vaswani and his colleagues at Google Research were instrumental in the development of multi-headed attention as part of their work on the transformer model. Their contribution has significantly influenced subsequent developments in AI models and applications across various fields.