// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Soft Prompt
A special type of prompt that consists of continuous, learnable numerical vectors instead of actual words, used to subtly guide an AI model without explicit text instructions.
TECHNICAL DEFINITION
A Soft Prompt is a sequence of continuous, trainable vector embeddings prepended to the input of a frozen large language model (LLM), optimized directly through gradient descent to guide the model's behavior for specific tasks, distinguishing it from discrete, human-readable text prompts.
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
- Continuous Prompt
- Embeddings Prompt
- Learnable Prompt
- Virtual Token
USAGE NOTE
Soft prompts are a core component of parameter-efficient fine-tuning methods like Prefix Tuning and Prompt Tuning.
DEVELOPERS
Organizations developing technology related to Soft Prompt.
Pioneers in large language model research, they have extensively published on and implemented methods like prompt tuning and prefix tuning, which involve learnable continuous prompts (soft prompts) for efficient model adaptation and task-specific performance.
Actively researches and applies various prompt-based learning methods, including those that leverage continuous, learnable prompt representations for efficient model adaptation and to improve performance across diverse tasks.
Conducts advanced research into large language model efficiency, adaptation, and prompting strategies, including methods that utilize soft prompts (e.g., prompt tuning) for fine-tuning and task-specific performance without full model retraining.
Provides open-source libraries (e.g., Transformers, PEFT) and tools that enable the research and practical implementation of soft prompts, prompt tuning, and other parameter-efficient fine-tuning techniques for large language models, fostering community development.
A leading academic research institution that extensively publishes on and investigates various aspects of foundation models, including prompt engineering, prompt tuning, and the use of soft prompts for model adaptation and evaluation.
A non-profit AI research institute known for its contributions to natural language processing and deep learning, including research into efficient model adaptation techniques that can involve continuous or soft prompts.
Develops platforms and software (e.g., NeMo) that support the development and deployment of large language models, incorporating research-backed techniques like soft prompt tuning to optimize model performance and efficiency for various AI applications.
Engages in research and development of large language models and their deployment services, where techniques like soft prompts are relevant for efficient customization and adaptation of models for specific enterprise use cases via services like Amazon Bedrock and SageMaker.