Prompt Tuning Method

Prompt tuning is a parameter-efficient fine-tuning method for pre-trained language and vision-language models, aiming to improve performance on downstream tasks by learning small sets of "soft prompts" rather than modifying the entire model. Current research focuses on optimizing prompt generation strategies, including methods that leverage attributes, instructions, or even subgraph-level information, and on addressing challenges like effective modal alignment in multi-modal models and mitigating overfitting in few-shot scenarios. This approach offers significant advantages in terms of computational efficiency and reduced memory footprint, making it a valuable technique for adapting large pre-trained models to diverse applications with limited resources.