  Prompt learning has emerged as an efficient alternative for fine-tuning
foundational models, such as CLIP, for various downstream tasks. However, there
is no work that provides a comprehensive explanation for the working mechanism
of the multi-modal prompts. In this paper, we conduct a direct analysis of the
multi-modal prompts by asking the following questions: $(i)$ How do the learned
multi-modal prompts improve the recognition performance? $(ii)$ What do the
multi-modal prompts learn? To answer these questions, we begin by isolating the
component of the formula where the prompt influences the calculation of
self-attention at each layer in two distinct ways, \ie, $(1)$ introducing
prompt embeddings makes the $[cls]$ token focus on foreground objects. $(2)$
the prompts learn a bias term during the update of token embeddings, allowing
the model to adapt to the target domain. Subsequently, we conduct extensive
visualization and statistical experiments on the eleven diverse downstream
recognition datasets. From the experiments, we reveal that the learned prompts
improve the performance mainly through the second way, which acts as the
dataset bias to improve the recognition performance of the pre-trained model on
the corresponding dataset. Meanwhile, we propose the bias tuning way to
validate our finding. With a deeper understanding of the multi-modal prompt, we
hope our work can inspire new and solid research in this direction.
