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

[2605.23694] ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2605.23694 (cs) [Submitted on 22 May 2026 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models Authors: Fen Wang , Zekai Shao , Qiman Kang , Chunran Hu , Zhixuan Zhang , Lexu Xie , Chao Liu , Siming Chen View a PDF of the paper titled ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models, by Fen Wang and 7 other authors View PDF HTML (experimental) 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 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. Comments: Accepted by VIS 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2605.23694 [cs.CL] (or arXiv:2605.23694v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2605.23694 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Fen Wang [ view email ] [v1] Fri, 22 May 2026 14:49:48 UTC (5,477 KB) [v2] Wed, 10 Jun 2026 14:24:51 UTC (5,476 KB) [v3] Sat, 15 Aug 2026 13:08:56 UTC (6,982 KB) Full-text links: Access Paper: View a PDF of the paper titled ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models, by Fen Wang and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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