Abstract
Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perform poorly as judges, limiting their real-world use in resource-constrained settings. To address this, we propose two approaches to ensure cost-efficient evaluation: (i) multi-criteria prompting, which combines separate evaluation criteria into a single query, and (ii) domain-adaptive transfer learning, in which we fine-tune a 2B-parameter LVLM on synthetic judgments in a chart dataset to create the ChartJudge. Experiments show that multi-criteria prompting exposes robustness gaps, which led to a huge drop in performance for 7B models, including specialized LVLM judges like LLaVA-Critic. In addition, we find that our tiny LVLM (ChartJudge) can effectively transfer knowledge from one dataset to another to make it a more specialized model. Our fine-grained analysis across chart types and query complexities offers actionable insights into trade-offs between model size, prompt design, and transferability, enabling scalable, low-cost evaluation for chart reasoning tasks. Our code and the data will be made publicly available.
Abstract (translated)
具有70亿参数的大规模视觉-语言模型（LVLMs）在图表理解任务中作为自动评判工具方面显示出了潜力。然而，小型模型（<=20亿参数）在评判任务中的表现仍然不佳，限制了它们在资源受限环境下的实际应用。为了解决这一问题，我们提出了两种确保成本效益评估的方法：(i) 多标准提示法，这种方法将不同的评价标准合并到一个查询中；以及(ii) 领域适应性迁移学习，在此过程中我们在图表数据集上对20亿参数的LVLM进行微调以创建ChartJudge。实验表明，多标准提示法揭示了模型鲁棒性的差距，导致包括专门化的LVLM评判器（如LLaVA-Critic）在内的70亿参数模型性能大幅下降。此外，我们发现我们的小型LVLM（ChartJudge）能够将一个数据集的知识转移到另一个数据集中，使其成为一个更加专业化的模型。我们在不同图表类型和查询复杂度上的细致分析提供了关于模型大小、提示设计以及迁移能力之间权衡的实用见解，从而为图表推理任务提供可扩展且低成本的评估方法。我们的代码和数据将会公开发布。
URL
https://arxiv.org/abs/2510.07545
