ChartCap: Mitigating Hallucination of Dense Chart Captioning
Authors: name: Junyoung Lim, name: Jaewoo Ahn, name: Gunhee Kim

Generating accurate, informative, and hallucination-free captions for charts
remains challenging for vision language models, primarily due to the lack of
large-scale, high-quality datasets of real-world charts. However, existing
real-world chart datasets suffer from the inclusion of extraneous information
that cannot be inferred from the chart and failure to sufficiently capture
structural elements and key insights. Therefore, we introduce ChartCap, a
large-scale dataset of 565K real-world chart images paired with type-specific,
dense captions that exclude extraneous information and highlight both
structural elements and key insights in detail. To build ChartCap, we design a
four-stage pipeline that generates captions using only the discernible data
from the chart and employ a cycle consistency-based human verification, which
accelerates quality control without sacrificing accuracy. Additionally, we
propose a novel metric, the Visual Consistency Score, which evaluates caption
quality by measuring the similarity between the chart regenerated from a
caption and the original chart, independent of reference captions. Extensive
experiments confirms that models fine-tuned on ChartCap consistently generate
more accurate and informative captions with reduced hallucinations, surpassing
both open-source and proprietary models and even human-annotated captions.