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Existing automatic evaluation on text-to-image synthesis can only provide animage-text matching score, without considering the object-levelcompositionality, which results in poor correlation with human judgments. Inthis work, we propose LLMScore, a new framework that offers evaluation scoreswith multi-granularity compositionality. LLMScore leverages the large languagemodels (LLMs) to evaluate text-to-image models. Initially, it transforms theimage into image-level and object-level visual descriptions. Then an evaluationinstruction is fed into the LLMs to measure the alignment between thesynthesized image and the text, ultimately generating a score accompanied by arationale. Our substantial analysis reveals the highest correlation of LLMScorewith human judgments on a wide range of datasets (Attribute Binding Contrast,Concept Conjunction, MSCOCO, DrawBench, PaintSkills). Notably, our LLMScoreachieves Kendall's tau correlation with human evaluations that is 58.8% and31.2% higher than the commonly-used text-image matching metrics CLIP and BLIP,respectively.

LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation