# Graph-Based Multimodal Contrastive Learning for Chart Question Answering Source: [Graph-Based Multimodal Contrastive Learning for Chart Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:28:27.917Z Authors: Yue Dai, Soyeon Caren Han, Wei Liu Published: 2025-01-08T06:27:07.000Z ## Abstract Chart question answering (ChartQA) is challenged by the heterogeneous composition of chart elements and the subtle data patterns they encode. This work introduces a novel joint multimodal scene graph framework that explicitly models the relationships among chart components and their underlying structures. The framework integrates both visual and textual graphs to capture structural and semantic characteristics, while a graph contrastive learning strategy aligns node representations across modalities enabling their seamless incorporation into a transformer decoder as soft prompts. Moreover, a set of tailored Chain of Thought (CoT) prompts is proposed to enhance multimodal large language models (MLLMs) in zero-s ot scenarios by mitigating hallucinations. Extensive evaluations on benchmarks including ChartQA, OpenCQA, and ChartX demonstrate significant performance improvements and validate the efficacy of the proposed approach. ## 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]()