Soft Prompt Tuning

Soft prompt tuning is a parameter-efficient technique for adapting pre-trained language models (and increasingly, multimodal models) to new tasks by learning small sets of embedding vectors ("soft prompts") that are prepended to input data, rather than modifying the model's weights. Current research focuses on improving the design and learning of these soft prompts, exploring techniques like prompt normalization, multi-token embedding superposition, and incorporating task-specific or context-aware information into prompt generation. This approach offers significant advantages in terms of computational efficiency and reduced memory footprint, making it particularly valuable for resource-constrained settings and facilitating the adaptation of large models to diverse downstream applications.