Existing automatic evaluation on text-to-image synthesis can only provide an
image-text matching score, without considering the object-level
compositionality, which results in poor correlation with human judgments. In
this work, we propose LLMScore, a new framework that offers evaluation scores
with multi-granularity compositionality. LLMScore leverages the large language
models (LLMs) to evaluate text-to-image models. Initially, it transforms the
image into image-level and object-level visual descriptions. Then an evaluation
instruction is fed into the LLMs to measure the alignment between the
synthesized image and the text, ultimately generating a score accompanied by a
rationale. Our substantial analysis reveals the highest correlation of LLMScore
with human judgments on a wide range of datasets (Attribute Binding Contrast,
Concept Conjunction, MSCOCO, DrawBench, PaintSkills). Notably, our LLMScore
achieves Kendall's tau correlation with human evaluations that is 58.8% and
31.2% higher than the commonly-used text-image matching metrics CLIP and BLIP,
respectively.

LLMScore 利用大语言模型为文本到图像合成模型提供带有多层次的组成性评估得分，并且与通常使用的文本 - 图像匹配度量相比，其与人类评估之间的相关性显著更高。