LLMScore: Unveiling the Power of Large Language Models in Text-to-Image
  Synthesis Evaluation
Yujie Lu Xianjun Yang Xiujun Li Xin Eric Wang William Yang Wang
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.