The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance (2504.10179v1)

Abstract: Multimodal LLMs (MLLMs) are set to transform how machines process and generate human-like responses by integrating diverse modalities such as text, images, and code. 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. While Large MLLMs excel in structured tasks such as code generation, achieving accuracies up to 96.88% under Few-Shot prompting, all models struggle with complex reasoning and abstract understanding, often yielding accuracies below 60% and high hallucination rates. Structured reasoning prompts frequently increased hallucination up to 75% in small models and led to longer response times (over 20 seconds in Large MLLMs), while simpler prompting methods provided more concise and efficient outputs. No single prompting method uniformly optimises all task types. Instead, adaptive strategies combining example-based guidance with selective structured reasoning are essential to enhance robustness, efficiency, and factual accuracy. Our findings offer practical recommendations for prompt engineering and support more reliable deployment of MLLMs across applications including AI-assisted coding, knowledge retrieval, and multimodal content understanding.

• The paper establishes that adaptive prompting strategies improve robustness and factual accuracy across diverse multimodal tasks.
• The paper evaluates seven prompting methods on 13 models with varying scales from small (<4B) to large (>10B) using tasks in reasoning and code generation.
• The paper demonstrates that Few-Shot prompting can achieve up to 96.88% accuracy for structured tasks, illustrating effective trade-offs in prompt engineering.

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

Introduction

The integration of multimodal inputs within LLMs has led to the evolution of Multimodal LLMs (MLLMs), enabling sophisticated multimodal reasoning abilities. While traditional LLMs have primarily focused on text-based data, MLLMs absorb a variety of inputs, such as text, images, and potentially audio, expanding their application range significantly. Effective utilization of these models is highly contingent upon the implementation of prompt engineering techniques. This paper presents an evaluation of seven distinct prompt methodologies applied across 13 open-source MLLMs, meticulously categorizing results by parameter count (Small $<4$B, Medium 4B–10B, and Large $>10$B) and employing tasks across reasoning, compositionality, and more.

MLLM Architecture and Applications

MLLM architectures inherently require more complex integration than traditional LLMs due to the diverse data types they process. The typical MLLM includes modality encoders, transformation layers, and an LLM backbone, tasked with merging encoded features into coherent outputs.

Figure 1: A high-level overview of a typical MLLM pipeline. Multiple input modalities are first processed by dedicated encoders, followed by feature transformation and finalized by a backbone integrating these multimodal features.

Notably, this paper highlights the rapid development of MLLMs that specialize in integrating multiple modalities, with specific architectures like ViT and CLIP playing key roles in feature extraction. Current proprietary and open-source models exhibit vast differences in flexibility, scalability, and performance across diverse tasks, demonstrating varying levels of instruction-following abilities.

Methods

The researchers implemented a multi-staged framework to evaluate the impact of prompt engineering techniques on various task performances, using different metrics related to task dimensions and model scales. The experimental setup comprised of the following stages:

1. Defining Core Evaluation Aspects: Four key dimensions were selected, spanning reasoning and compositionality to multimodal understanding and complex code generation.
2. Model Selection: Thirteen open-source MLLM models were chosen, representing a diverse set of architectural designs and parameter scales.
3. Prompt Engineering Methods: Seven prompting methods—including Zero-Shot, One-Shot, Few-Shot, and Chain-of-Thought—were rigorously applied.
4. Evaluation Framework: Implementation considerations focused on task efficacy and computational resource management, with careful attention given to reducing hallucination rates and improving factual accuracy.

Results

The paper showcases distinct outcomes for each model category and prompting method, highlighting trade-offs between computational efficiency and output quality. Large models frequently excelled in structured tasks such as code generation, achieving up to 96.88% accuracy with Few-Shot prompting. Conversely, tasks requiring complex reasoning demonstrated notable challenges, with hallucination rates as high as 75% in smaller models during structured reasoning prompts (e.g., Tree-of-Thought).

Discussion

Adaptive prompting strategies were highlighted as necessary, given that no single method uniformly optimized all task types. Instead, strategies that combine example-based approaches with selective structured reasoning significantly enhance performance reliability and accuracy. This reflects the theoretical and practical implications of the research, positioning adaptive strategies as superior for deploying MLLMs in real-world applications like AI-assisted coding or knowledge retrieval.

Conclusion

The paper represents a comprehensive evaluation across diverse MLLM architectures and task types, underscoring the intricate balance required in prompt engineering to optimize model performance. Fueled by adaptive strategies, MLLMs promise improved robustness and factual accuracy, bolstering their potential for real-world application across various domains. Future work will likely elaborate on these strategies, aiming to push the boundaries of MLLM capabilities further.