## 2 Related Work
### Challenges and Opportunities in Using LLMs for Evaluating Text-to-Image Generation Models  
Evaluation of text-to-image generation models, such as DALL-E and Stable Diffusion, is a critical but challenging task, traditionally relying on human assessments or automated metrics like CLIP-based alignment scores. Human evaluation, while effective, is costly, time-consuming and inherently subjective, while conventional automated metrics often fail to capture nuanced attributes such as compositionality, creativity, or aesthetic quality. Large language models (LLMs), such as GPT-4, have emerged as promising tools to complement or replace traditional evaluation methods, offering potential advantages in scalability, consistency, and explainability [4; 5]. However, significant questions remain about their reliability, human alignment, and ability to perform in multimodal contexts. Research in this area has coalesced around the use of GPT-4 or similar LLMs for evaluation tasks, often employing textual descriptions of images or direct multimodal inputs via advanced vision language models (e.g., GPT-4 Vision). LLMScore [4] introduced a novel framework for evaluating text-to-image models by converting images into object-level and image-level descriptions and using GPT-4 to assess alignment with textual prompts. This approach demonstrated a better correlation with human judgments compared to metrics such as CLIP and BLIP, particularly for compositional and fine-grained evaluation tasks. Building on such frameworks, GPT-4 Vision (GPT-4V) was tested as a multitask evaluator capable of scoring both text-to-image and image-to-text tasks; it achieved promising results, but exhibited gaps in handling complex real-world reasoning and fine-grained visual clarity [5; 6]. Explainability has been central to the use of LLMs as evaluators. Frameworks like X-IQE [7] and VIEScore [8] leverage hierarchical reasoning techniques, such as chain-of-thought (CoT) prompting, to generate detailed rationales alongside scores, enhancing transparency in subjective areas like aesthetics and creativity. Meanwhile, task-specific fine-tuning has demonstrated notable improvements in alignment and robustness. EvalAlign [6], for example, fine-tuned multimodal GPT-4V models to better align with human judgments in dimensions of faithfulness and image-text coherence. Despite their potential, challenges persist in deploying LLMs as reliable evaluators. Biases, such as favoring LLM-generated content [9] or inconsistencies between repeated evaluations [10], and difficulties with tasks highly knowledge-intensive or ambiguous [11] underscore the need for continued refinement. Efforts such as T2I-FactualBench [11] and DyEval [12] address these gaps by introducing evaluation frameworks that emphasize factual consistency and user interaction. Furthermore, balancing the computational cost of proprietary models such as GPT-4V and the reduced performance of open source alternatives, such as MiniGPT-4 [13], remains an area of active exploration [14]. This synthesis highlights an evolving research trajectory that integrates advanced multimodal reasoning, fine-tuning, and explainability into LLM-based evaluation frameworks. Although studies demonstrate high human alignment and scalability, combining these models with hybrid human-in-the-loop systems and customized benchmarks may be necessary to address existing limitations. These developments establish GPT-4 and similar LLMs as promising tools in evaluating text-to-image generation models, establishing a foundation for further innovation and practical deployment.