ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models
arXiv:2605.23694v1 Announce Type: new Abstract: 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 capt
The proliferation of Multimodal Large Language Models (MLLMs) and their increasing adoption for automated content generation, like chart descriptions, necessitates a critical evaluation of their output quality. This benchmarking effort emerges as the technology matures and its integration into various applications becomes more widespread.
Evaluating the faithfulness and insightfulness of MLLM-generated chart descriptions is crucial for ensuring accessibility, accuracy in data interpretation, and effective information retrieval, directly impacting the reliability and utility of AI-powered tools in data analysis. Poor generation quality undermines trust and utility.
This research introduces new benchmarks to more rigorously assess the quality of MLLM outputs, shifting the focus from simple fact enumeration to the deeper attributes of faithfulness and insightfulness in chart descriptions. It will influence MLLM development towards more robust and reliable descriptive capabilities.
- · AI developers focused on quality and reliability
- · Data visualization platforms
- · Users relying on MLLM-generated accessibility features
- · Researchers in explainable AI
- · MLLMs with poor interpretability and accuracy
- · AI applications generating shallow chart descriptions
- · Platforms without robust ground truth for evaluation
Improved MLLMs will generate more sophisticated and trustworthy chart descriptions, enhancing data accessibility.
This will lead to greater adoption of MLLMs in critical analytical and reporting functions, impacting professional workflows.
Higher quality automated descriptions could fundamentally alter how data is consumed and understood across sectors, potentially democratizing complex data analysis more broadly.
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Read at arXiv cs.CL