# mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning Source: [mChartQA: A universal benchmark for multimodal Chart Question Answer based on Vision-Language Alignment and Reasoning]() Collection index: [llms.txt]() Data updated: 2026-08-12T05:00:13.408Z Authors: Jingxuan Wei, Nan Xu, Guiyong Chang, Yin Luo, BiHui Yu, Ruifeng Guo Published: 2024-04-02T01:28:44.000Z ## Abstract In the fields of computer vision and natural language processing, multimodal chart question-answering, especially involving color, structure, and textless charts, poses significant challenges. Traditional methods, which typically involve either direct multimodal processing or a table-to-text conversion followed by language model analysis, have limitations in effectively handling these complex scenarios. This paper introduces a novel multimodal chart question-answering model, specifically designed to address these intricate tasks. Our model integrates visual and linguistic processing, overcoming the constraints of existing methods. We adopt a dual-phase training approach: the initial phase focuses on aligning image and text representations, while the subsequent phase concentrates on optimizing the model's interpretative and analytical abilities in chart-related queries. This approach has demonstrated superior performance on multiple public datasets, particularly in handling color, structure, and textless chart questions, indicating its effectiveness in complex multimodal tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()