## 2 Related Works  
MLLMs [21, 22, 23, 24, 25, 26], which aim to generate text by processing diverse multimodal inputs through LLMs, have been the focus of extensive research in terms of architectural advancements [1, 15] and training methodologies [1]. Despite continuous improvements in performance, MLLMs still face significant challenges, particularly in regard to generating hallucinated responses that fail to accurately reflect the provided visual or contextual inputs [12]. To mitigate these limitations, prompt engineering (PE) strategies have emerged as a promising solution to enhance response quality and reliability [14, 15, 16]. Additionally, recent research has explored user experience optimization methods specifically tailored for MLLMs, employing diverse evaluation frameworks to assess model effectiveness [11, 33, 34, 35]. In terms of architectural studies, [PERSON] et al. [1] categorized MLLM architectures into three core components: pre-trained modality encoders, pre-trained LLMs, and cross-modality transformers. This structure allows MLLMs to integrate multimodal information efficiently while leveraging LLMs' textual reasoning capabilities. In contrast, [PERSON] et al. [15] pro posed a more fine-grained MM-LLM framework, decomposing it into modality encoders, input projectors, LLM backbones, output projectors, and modality generators. This expanded framework extends beyond text generation, encompassing multimodal content generation, including images and audio, thus broadening the application scope of MLLMs. In contrast to architectural research, studies on MLLM training methodologies have primarily focused on pretraining, instruction tuning, and alignment tuning [1]. Pretraining aims to align different modalities while embedding multimodal world knowledge into the model [1]. Instruction tuning is designed to teach MLLMs how to follow user instructions and effectively perform assigned tasks. Meanwhile, alignment tuning ensures that MLLMs are aligned with specific human preferences, improving their ability to generate responses that are both reliable and contextually appropriate. Despite these advanced training strategies, achieving perfect alignment between different modality encoders remains a fundamental challenge. This misalignment issue often leads to multimodal hallucination, where the content generated by an MLLM does not accurately correspond to the provided visual input. When combined with an LLM, multimodal hallucination can manifest in three distinct types [12]: existence hallucination, attribute hallucination, and relationship hallucination. Existence hallucination is the most fundamental form of hallucination, where the model incorrectly asserts the presence of objects that do not actually exist in the image. Attribute hallucination occurs when the model misdescribes the attributes of an object, such as failing to correctly identify the color of a dog. This type of hallucination is often correlated with existence hallucination, as attribute descriptions should be grounded in the actual objects present in the image. Relationship hallucination is a more complex phenomenon that extends beyond the existence of objects. It refers to incorrect descriptions of relationships between objects, such as relative positioning or interactions, leading to misinterpretations of the scene's contextual meaning. These hallucination challenges highlight the inherent difficulties in aligning multimodal representations, necessitating effective prompt engineering strategies to mitigate the issue and improve response reliability. To help address the hallucination problem in LLMs, various prompt engineering techniques such as ICL [17], CoT [18], SSR [19], ToT [19], and RAG [20] have been introduced. These approaches aim to guide the model in structured reasoning, contextual retrieval, and incremental step-wise reasoning, thereby improving response accuracy.