构建可生成精准解释的图表问答数据集，提升模型理解与可信度。
ChartQA-X: Generating Explanations for Visual Chart Reasoning
- 构建4类30,799张图表的解释型问答数据集
- 模型生成解释比人工更准确逻辑更强，质量提升24.57点
- 适合需要可信视觉分析的AI助手研发者
从图表图像中解释复杂信息对数据驱动决策至关重要。本文提出ChartQA-X，一个包含30,799个图表样本的数据集，覆盖四种图表类型，每张图配有上下文相关的问答及解释。解释通过忠实性、信息量、连贯性和困惑度等指标生成与筛选。245名参与者的人工评估显示，模型生成的解释在准确性和逻辑性上超越人工写作，清晰度与整体质量相当。微调后的模型在多个指标上显著提升，解释质量最高提升24.57点，问答准确率提高18.96个百分点，在未见基准上提升14.75个百分点。结合解释与答案可有效传达复杂视觉信息，增强理解与信任。
原文摘要 · Abstract (English)
The ability to explain complex information from chart images is vital for effective data-driven decision-making. In this work, we address the challenge of generating detailed explanations alongside answering questions about charts. We present ChartQA-X, a comprehensive dataset comprising 30,799 chart samples across four chart types, each paired with contextually relevant questions, answers, and explanations. Explanations are generated and selected based on metrics such as faithfulness, informativeness, coherence, and perplexity. Our human evaluation with 245 participants shows that model-generated explanations in ChartQA-X surpass human-written explanations in accuracy and logic and are comparable in terms of clarity and overall quality. Moreover, models fine-tuned on ChartQA-X show substantial improvements across various metrics, including absolute gains of up to 24.57 points in explanation quality, 18.96 percentage points in question-answering accuracy, and 14.75 percentage points on unseen benchmarks for the same task. By integrating explanatory narratives with answers, our approach enables agents to convey complex visual information more effectively, improving comprehension and greater trust in the generated responses.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。