// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Encoder-Decoder
This architecture consists of two main parts: an encoder that processes the input and a decoder that uses that processed information to generate an output.
TECHNICAL DEFINITION
A neural network architecture comprising an encoder, which maps an input sequence to a fixed-length context vector, and a decoder, which generates an output sequence based on this context vector, commonly used for sequence-to-sequence tasks.
BACKGROUND
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Because self-attention alone is permutation-invariant, transformers inject positional information, typically through positional encodings or learned positional embeddings, so token order can affect the output.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Seq2seq model
- sequence-to-sequence
- encoder-decoder framework
USAGE NOTE
Encoder-decoder models are widely used in machine translation, text summarization, and image captioning.
DEVELOPERS
Organizations developing technology related to Encoder-Decoder.
Pioneers of the Transformer architecture, which are advanced encoder-decoder models. They develop large language models like T5 (Text-to-Text Transfer Transformer), which is an encoder-decoder model, and contribute significantly to the theoretical and applied aspects of these models, crucial for prompt engineering.
Engages in fundamental AI research, including developing encoder-decoder models like BART and NLLB (No Language Left Behind). Their work contributes directly to advanced NLP capabilities used in AI engineering and prompt design.
Actively researches and develops various transformer-based models, including encoder-decoder architectures like PEGASUS for abstractive summarization. Their work is integrated into Azure AI services, supporting AI engineering and prompt design applications.
Utilizes and develops encoder-decoder architectures for services like machine translation, text summarization, and other NLP tasks within AWS AI. These models are foundational for many AI engineering efforts and prompt-based applications.
A leading platform for AI engineering, providing open-source libraries (Transformers library) and models, many of which are encoder-decoder based (e.g., T5, BART). They enable practitioners to easily access, fine-tune, and deploy these models for various prompt design tasks.
A major AI research arm, especially strong in NLP and machine translation. They develop and deploy advanced models, many of which leverage encoder-decoder architectures, for various AI engineering applications.
Conducts research and develops novel NLP models, often building upon or extending transformer and encoder-decoder principles. Their work contributes to the advancement of AI engineering practices.
Undertakes significant research in core AI technologies, including natural language processing and the development of deep learning architectures, which frequently involve encoder-decoder structures for tasks like machine translation and text generation.