// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Sequence to Sequence
Sequence to Sequence models take one sequence of data, like an English sentence, and transform it into another sequence, like a French sentence.
TECHNICAL DEFINITION
A Sequence to Sequence (Seq2Seq) model is an encoder-decoder neural network architecture designed to map an input sequence to an output sequence, commonly used in machine translation and text summarization.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Seq2Seq
- Encoder-Decoder
- Sequence transformation
USAGE NOTE
Widely used for tasks like machine translation, text summarization, and chatbots.
DEVELOPERS
Organizations developing technology related to Sequence to Sequence.
A division of Google focused on fundamental AI research and application, including pioneering work on Sequence to Sequence models, Transformers, and their use in products like Google Translate, LaMDA, PaLM, and Gemini, which are heavily influenced by prompt design.
Developers of large language models like the GPT series, which are built upon Transformer (a type of Sequence to Sequence) architectures. Their work directly emphasizes prompt engineering for controlling model output.
Meta's AI research division, actively developing and releasing open-source large language models such as LLaMA and OPT, which are based on Sequence to Sequence principles and contribute to advancements in generative AI and prompt design.
Microsoft's divisions focused on AI research and cloud services, developing and integrating large language models based on Sequence to Sequence architectures into products like Microsoft Copilot, Bing Chat, and Azure AI services, requiring advanced AI engineering and prompt design.
A leading platform and community for machine learning, providing the Transformers library which is essential for AI engineers working with Sequence to Sequence models. They also offer tools and resources for model deployment and prompt engineering.
Developers of the Claude family of large language models, which leverage Sequence to Sequence (Transformer) architectures. Their research focuses on creating safe and helpful AI, with prompt engineering being critical for aligning model behavior.
Salesforce's research division focused on advancing AI for enterprise applications. They conduct research and develop models, including those based on Sequence to Sequence architectures, for various NLP tasks and generative AI solutions.
IBM's long-standing research arm, with significant contributions to natural language processing and generative AI, including work on Sequence to Sequence models and their application in areas like machine translation and dialogue systems.