标题： VisFinEval：一种面向全面金融理解的场景驱动中文多模态基准 显示英文标题

标题： VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding

摘要： 多模态大语言模型（MLLMs）在自动化复杂金融分析方面具有巨大潜力。 为了全面评估它们的能力，我们引入了VisFinEval，这是首个大规模中文基准，涵盖了金融任务的整个前中后台生命周期。 VisFinEval包含从八种常见金融图像模态（例如，K线图、财务报表、官方印章）中提取的15,848个带注释的问答对，分为三个层次场景深度：金融知识与数据分析、金融分析与决策支持、金融风险控制与资产优化。 我们在零样本设置下评估了21个最先进的MLLMs。 排名第一的模型Qwen-VL-max总体准确率达到76.3%，超过了非专家人类但比金融专家低超过14个百分点。 我们的错误分析揭示了六个常见的失败模式——包括跨模态错位、幻觉和业务流程推理的失误——这些突显了未来研究的关键方向。 VisFinEval旨在加速开发能够无缝整合文本和视觉金融信息的稳健、领域定制的MLLMs。 数据和代码可在https://github.com/SUFE-AIFLM-Lab/VisFinEval获取。 显示英文摘要

摘要： Multimodal large language models (MLLMs) hold great promise for automating complex financial analysis. To comprehensively evaluate their capabilities, we introduce VisFinEval, the first large-scale Chinese benchmark that spans the full front-middle-back office lifecycle of financial tasks. VisFinEval comprises 15,848 annotated question-answer pairs drawn from eight common financial image modalities (e.g., K-line charts, financial statements, official seals), organized into three hierarchical scenario depths: Financial Knowledge & Data Analysis, Financial Analysis & Decision Support, and Financial Risk Control & Asset Optimization. We evaluate 21 state-of-the-art MLLMs in a zero-shot setting. The top model, Qwen-VL-max, achieves an overall accuracy of 76.3%, outperforming non-expert humans but trailing financial experts by over 14 percentage points. Our error analysis uncovers six recurring failure modes-including cross-modal misalignment, hallucinations, and lapses in business-process reasoning-that highlight critical avenues for future research. VisFinEval aims to accelerate the development of robust, domain-tailored MLLMs capable of seamlessly integrating textual and visual financial information. The data and the code are available at https://github.com/SUFE-AIFLM-Lab/VisFinEval.