FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models
Liqiang Jing Ruosen Li Yunmo Chen Mengzhao Jia Xinya Du
We introduce FAITHSCORE (Faithfulness to Atomic Image Facts Score), a
reference-free and fine-grained evaluation metric that measures the
faithfulness of the generated free-form answers from large vision-language
models (LVLMs). The FAITHSCORE evaluation first identifies sub-sentences
containing descriptive statements that need to be verified, then extracts a
comprehensive list of atomic facts from these sub-sentences, and finally
conducts consistency verification between fine-grained atomic facts and the
input image. Meta-evaluation demonstrates that our metric highly correlates
with human judgments of faithfulness. We collect two benchmark datasets (i.e.
LLaVA-1k and MSCOCO-Cap) for evaluating LVLMs instruction-following
hallucinations. We measure hallucinations in state-of-the-art LVLMs with
FAITHSCORE on the datasets. Results reveal that current systems are prone to
generate hallucinated content unfaithful to the image, which leaves room for
future improvements. Further, we find that current LVLMs despite doing well on
color and counting, still struggle with long answers, relations, and multiple
objects.