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[2510.09201] Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Summary
This article introduces the concept of multimodal prompt optimization for Multimodal Large Language Models (MLLMs), proposing a new framework that enhances prompt crafting across various modalities, including text, images, and videos.
Why It Matters
As MLLMs gain traction, optimizing prompts across multiple modalities is crucial for maximizing their potential. This research addresses a significant gap in current methodologies, providing a unified framework that can improve performance and efficiency in multimodal applications.
Key Takeaways
- Multimodal prompt optimization expands traditional prompt crafting to include various data types beyond text.
- The proposed Multimodal Prompt Optimizer (MPO) utilizes a Bayesian-based selection strategy for effective prompt alignment.
- Experiments show MPO outperforms existing text-only optimization methods across diverse modalities.
- This research highlights the importance of integrating multimodal capabilities in LLMs for enhanced performance.
- The findings could influence future developments in AI applications that require multimodal understanding.
Computer Science > Machine Learning arXiv:2510.09201 (cs) [Submitted on 10 Oct 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs Authors:Yumin Choi, Dongki Kim, Jinheon Baek, Sung Ju Hwang View a PDF of the paper titled Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs, by Yumin Choi and 3 other authors View PDF HTML (experimental) Abstract: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 modaliti...