RADAR: A Reasoning-Guided Attribution Framework for Explainable Visual Data Analysis

Anku Rani, Aparna Garimella, Apoorv Saxena, Balaji Vasan Srinivasan, Paul Pu Liang

Keywords: Mathematical Reasoning, Attribution, Multimodal Large Language Models

Abstract: Data visualizations, such as charts, are fundamental tools for quantitative analysis and decision-making across various 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. We introduce RADAR, a semi-automatic approach to obtain a benchmark dataset comprising 17,819 diverse samples with charts, questions, reasoning steps, and 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 15% compared to baseline methods.

Submission Number: 314