Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for
  MLLMs
Authors: name: Yumin Choi, name: Dongki Kim, name: Jinheon Baek, name: Sung Ju Hwang
Organization: KAIST AI

Large Language Models (LLMs) have shown remarkable success, and their
multimodal expansions (MLLMs) further unlock capabilities spanning images,
videos, and other modalities beyond text. However, despite this shift, prompt
optimization approaches, designed to reduce the burden of manual prompt
crafting while maximizing performance, remain confined to text, ultimately
limiting the full potential of MLLMs. Motivated by this gap, we introduce the
new problem of multimodal prompt optimization, which expands the prior
definition of prompt optimization to the multimodal space defined by the pairs
of textual and non-textual prompts. To tackle this problem, we then propose the
Multimodal Prompt Optimizer (MPO), a unified framework that not only performs
the joint optimization of multimodal prompts through alignment-preserving
updates but also guides the selection process of candidate prompts by
leveraging earlier evaluations as priors in a Bayesian-based selection
strategy. Through extensive experiments across diverse modalities that go
beyond text, such as images, videos, and even molecules, we demonstrate that
MPO outperforms leading text-only optimization methods, establishing multimodal
prompt optimization as a crucial step to realizing the potential of MLLMs.