"Prompt tuning is an optimization technique to improve the performance of a pretrained large language model (LLM) without modifying its core architecture... you create a lightweight temporary model in front of your generative multimodal pretrained model."
"This technique uses soft prompts, which are extra parameters inserted at the beginning of input... you can make them interpretable by mapping vectors to their closest match in the token vocabulary."
"Generative multimodal models leveraged for highly specific tasks benefit the most from prompt tuning, allowing for accurate responses to detailed queries..."
"When prompt tuning, task-specific parameters you add to your inputs update independently of the AI because it is frozen. This way, you condition it to respond to specific prompts more effectively without retraining."
Collection
[
|
...
]