  Despite recent advances in multimodal pre-training for visual description,
state-of-the-art models still produce captions containing errors, such as
hallucinating objects not present in a scene. The existing prominent metric for
object hallucination, CHAIR, is limited to a fixed set of MS COCO objects and
synonyms. In this work, we propose a modernized open-vocabulary metric, ALOHa,
which leverages large language models (LLMs) to measure object hallucinations.
Specifically, we use an LLM to extract groundable objects from a candidate
caption, measure their semantic similarity to reference objects from captions
and object detections, and use Hungarian matching to produce a final
hallucination score. We show that ALOHa correctly identifies 13.6% more
hallucinated objects than CHAIR on HAT, a new gold-standard subset of MS COCO
Captions annotated for hallucinations, and 30.8% more on nocaps, where objects
extend beyond MS COCO categories. Our code is available at
https://davidmchan.github.io/aloha/.
