基准测试与增强文本到图像模型以生成早期算术教育中的视觉表示
Benchmarking and Enhancing Text-to-Image Models for Generating Visual Representations in Early Arithmetic Education
AI总结：
针对早期算术教育中的方程到视觉生成任务，构建了E2V-Bench基准并评估了现有T2I模型，发现其在计数和关系结构上存在严重错误，进而探索了基准引导的增强策略。
摘要：
AI系统越来越多地用于支持教育内容创作，但尚不清楚它们能否生成忠实代表其旨在教授的教学概念的输出。因此，我们引入了方程到视觉生成任务，与传统的图像生成不同，该任务要求从算术方程中生成具有教学意义的视觉内容，同时精确保留其数值和关系结构。根据对教师的访谈和教育材料的分析，我们构建了E2V-Bench基准，涵盖四种基于教学法的视觉类型，以及用于评估视觉正确性的自动指标。我们的评估显示，最近的文本到图像（T2I）模型在此任务上频繁失败，错误主要表现为对象计数不正确和关系结构破坏。在此基础上，我们探索了基准引导的增强策略。这些策略改进了代表性模型，但剩余的差距要求未来的T2I模型具备更强的数值和关系基础。
英文摘要：
AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully represent the pedagogical concepts they are intended to teach. Thus, we introduce equation-to-visual generation, a task that, in contrast to conventional image generation, requires producing pedagogically meaningful visuals from arithmetic equations while precisely preserving their numerical and relational structure. Informed by interviews with teachers and an analysis of educational materials, we construct E2V-Bench, a benchmark spanning four pedagogically grounded visual types, along with automatic metrics for evaluating visual correctness. Our evaluation reveals that recent text-to-image (T2I) models frequently fail on this task, with errors dominated by incorrect object counts and broken relational structure. Building on this, we explore benchmark-guided enhancement strategies. These strategies improve representative models, while the remaining gap calls for stronger numerical and relational grounding in future T2I models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
