Isn’t FLAN-T5 already a fine-tuned model

"One potential issue to consider is the interpretability of learned virtual tokens. Remember, because the soft prompt tokens can take any value within the continuous embedding vector space. The trained tokens don't correspond to any known token, word, or phrase in the vocabulary of the LLM. However, an analysis of the nearest neighbor tokens to the soft prompt location shows that they form tight semantic clusters. In other words, the words closest to the soft prompt tokens have similar meanings. The words identified usually have some meaning related to the task, suggesting that the prompts are learning word like representations."