  The evaluation of machine-generated image captions poses an interesting yet
persistent challenge. Effective evaluation measures must consider numerous
dimensions of similarity, including semantic relevance, visual structure,
object interactions, caption diversity, and specificity. Existing
highly-engineered measures attempt to capture specific aspects, but fall short
in providing a holistic score that aligns closely with human judgments. Here,
we propose CLAIR, a novel method that leverages the zero-shot language modeling
capabilities of large language models (LLMs) to evaluate candidate captions. In
our evaluations, CLAIR demonstrates a stronger correlation with human judgments
of caption quality compared to existing measures. Notably, on Flickr8K-Expert,
CLAIR achieves relative correlation improvements over SPICE of 39.6% and over
image-augmented methods such as RefCLIP-S of 18.3%. Moreover, CLAIR provides
noisily interpretable results by allowing the language model to identify the
underlying reasoning behind its assigned score. Code is available at
https://davidmchan.github.io/clair/
