@inproceedings{rani-etal-2026-radar,
    title = "{RADAR}: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis",
    author = "Rani, Anku  and
      Garimella, Aparna  and
      Saxena, Apoorv  and
      Srinivasan, Balaji Vasan  and
      Liang, Paul Pu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.299/",
    pages = "5657--5677",
    ISBN = "979-8-89176-386-9",
    abstract = "Data visualizations like charts are fundamental tools for quantitative analysis and decision-making across fields, requiring accurate interpretation and mathematical reasoning. The emergence of Multimodal Large Language Models (MLLMs) offers promising capabilities for automated visual data analysis, such as processing charts, answering questions, and generating summaries. However, they provide no visibility into which parts of the visual data informed their conclusions; this black-box nature poses significant challenges to real-world trust and adoption. In this paper, we take the first major step toward evaluating and enhancing the capabilities of MLLMs to attribute their reasoning process by highlighting the specific regions in charts and graphs that justify model answers. To this end, we contribute RADAR, a semi-automatic approach to obtain a benchmark dataset comprising 1000 charts, 2000 question-answer pairs, 3599 reasoning steps, and 11,220 attribution annotations. We also introduce a method that provides attribution for chart-based mathematical reasoning. Experimental results demonstrate that our reasoning-guided approach improves attribution accuracy by up to 15 percentage points compared to baseline methods, and enhanced attribution capabilities translate to stronger answer generation, achieving high semantic similarity (BERTScore $\sim$ 0.90) with ground truth responses. This advancement represents a significant step toward more interpretable and trustworthy chart analysis systems, enabling users to verify and understand model decisions through reasoning and attribution."
}