The Future of MLLM Prompting Is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance

Abstract

Multimodal Large Language Models (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modalities such as text, images, and code. In this study, we specifically focus on text–image multimodal reasoning and understanding, evaluating their performance across diverse task categories. Yet, effectively harnessing their capabilities hinges on optimal prompt engineering. We present a comprehensive experimental evaluation of seven prompt engineering methods applied to 13 open-source MLLMs over 24 tasks spanning Reasoning and Compositionality, Multimodal Understanding and Alignment, Complex Code Generation and Execution, and Knowledge Retrieval and Integration. Our approach stratifies models by parameter count into Small (< 4B), Medium (4B–10B), and Large (> 10B) categories and compares prompting techniques including Zero-Shot, One-Shot, Few-Shot, Chain-of-Thought, Analogical, Generated Knowledge, and Tree-of-Thought. Our experiments reveal that while Large MLLMs excel in structured tasks such as code generation and execution, achieving accuracies as high as 96.88% under Few-Shot prompting. In multimodal understanding and alignment (with relevance scores reaching 100% using Zero-Shot prompting), all models struggle with complex reasoning and abstract model understanding, often yielding accuracies below 60% and high hallucination rates. Notably, structured reasoning prompts (Chain-of-Thought, Analogical, Generated Knowledge and Tree-of-Thought) frequently increased hallucination up to 75% in small models and led to longer response times (exceeding 20 seconds in Large MLLMs), while simpler prompting methods (One-Shot and Few-Shot) provided more concise and efficient outputs. Our findings underscore that no single prompting method uniformly optimizes all task types. Instead, adaptive prompting strategies that combine the strengths of example-based guidance with selective structured reasoning are essential to enhance robustness, efficiency, and factual accuracy in MLLMs. Our work provides critical insights and actionable recommendations for optimizing prompt engineering in text–image multimodal contexts, paving the way for more reliable deployment of MLLMs in real-world applications ranging from AI-assisted coding and knowledge retrieval to visual–textual content understanding.