A decoder is a core component in artificial intelligence architectures that specializes in generating sequential outputs. Operating in an auto-regressive manner, it produces one element at a time while considering both the previously generated elements and the original input information. The decoder's distinctive feature is its masked attention mechanism, which ensures it can only access previously generated elements during the sequence generation process, preventing it from seeing future elements that haven't been created yet.
An example of an autoencoder architecture with a decoder
In many AI systems, particularly encoder-decoder architectures, decoders work alongside encoders and employ cross-attention mechanisms to maintain connection with the input sequence. While encoders process and compress input information into rich representations, decoders utilize these representations to generate appropriate outputs step by step. This architecture has proven especially effective in tasks like machine translation, text summarization, and code generation, with modern language models like GPT primarily utilizing decoder-based architectures, while others like T5 combine both encoders and decoders for enhanced performance.
Learn more about autoencoders, encoders and decoders in Lilian Weng's amazing blog post.
