FenWang/ChartFI-Bench

ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models

Chart descriptions are essential for accessibility, cross-modal retrieval, and assisting readers in extracting insights from complex visualizations. As multimodal large language models (MLLMs) are increasingly adopted for automated chart description generation, a critical question arises: how faithfully and insightfully do these models actually describe charts? Current benchmarks fall short on two fronts: existing datasets consist of simple, homogeneous charts paired with shallow, fact-enumerating descriptions; and prevailing metrics fail to capture the multi-faceted nature of description quality. To address these gaps, we present the Chart Faithfulness and Insightfulness Benchmark (ChartFI-Bench). We first summarize four dimensions that characterize high-quality chart descriptions: factual accuracy, salient feature emphasis, domain-informed guidance, and chart–text complementarity. Guided by these dimensions, we construct a high-quality benchmark comprising 896 chart--description pairs, which feature visually complex charts and semantically rich descriptions. Furthermore, we design four aligned evaluation metrics---Faithfulness, Coverage, Informativeness, and Acuity---to systematically assess the quality of descriptions across these dimensions. Experiments conducted on mainstream MLLMs demonstrate the effectiveness of the proposed framework and reveal common weaknesses among existing models.

Dataset Statistics

• Total: 896 chart images and 896 reference descriptions

Data Structure

Each record contains the following fields, in this order:

Field	Type	Description
id	string	Sample identifier derived from the original filename without its extension
image	Image	Chart image in its original resolution
description	string	Reference description of the chart's structural elements and key insights

Usage

Install the Hugging Face Datasets library:

pip install datasets

Load the benchmark:

from datasets import load_dataset

dataset = load_dataset("FenWang/ChartFI-Bench")
test_data = dataset["test"]

Paper

📄 Paper: ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models

🌐 GitHub: ChartFI

Citation

If you use this dataset, please cite:

@article{wang2026chartfi,
  title={ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models},
  author={Wang, Fen and Shao, Zekai and Kang, Qiman and Hu, Chunran and Zhang, Zhixuan and Xie, Lexu and Liu, Chao and Chen, Siming},
  journal={arXiv preprint arXiv:2605.23694},
  year={2026}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt this dataset for any purpose, including commercial use, as long as you provide appropriate attribution by citing our paper.