Soft prompts, on the other hand, are learned vectors. They exist only in the embedding space and do not correspond to actual tokens in the model’s vocabulary. They cannot be directly interpreted by humans. These are used exclusively in prompt tuning and are optimized for task performance, often outperforming hard prompts in accuracy but at the cost of interpretability.