  The ability to judge whether a caption correctly describes an image is a
critical part of vision-language understanding. However, state-of-the-art
models often misinterpret the correctness of fine-grained details, leading to
errors in outputs such as hallucinating objects in generated captions or poor
compositional reasoning. In this work, we explore Token-Level Confidence, or
TLC, as a simple yet surprisingly effective method to assess caption
correctness. Specifically, we fine-tune a vision-language model on image
captioning, input an image and proposed caption to the model, and aggregate
either algebraic or learned token confidences over words or sequences to
estimate image-caption consistency. Compared to sequence-level scores from
pretrained models, TLC with algebraic confidence measures achieves a relative
improvement in accuracy by 10% on verb understanding in SVO-Probes and
outperforms prior state-of-the-art in image and group scores for compositional
reasoning in Winoground by a relative 37% and 9%, respectively. When training
data are available, a learned confidence estimator provides further improved
performance, reducing object hallucination rates in MS COCO Captions by a
relative 30% over the original model and setting a new state-of-the-art.
