形状与上下文：考察人类与AI之间的模糊日文字符识别差距
Shape vs. Context: Examining Human--AI Gaps in Ambiguous Japanese Character Recognition
AI总结：
本研究通过比较人类与VLMs在模糊日文字符识别中的决策模式，揭示了形状上下文对人类与AI对齐的影响。
摘要：
高文本识别性能并不保证视觉-语言模型（VLMs）在解决模糊性时具有与人类相似的决策模式。我们通过连续插值的日本字符形状（通过β-VAE生成）直接比较人类和VLMs，以研究这一行为差距。我们估计单字符识别（仅形状任务）中的决策边界，并评估在形状上下文中VLMs的响应是否与人类判断一致（即在词级上下文中将模糊字符嵌入人类决策边界附近）。我们发现，在仅形状任务中，人类和VLMs的决策边界存在差异，并且在某些条件下，形状上下文可以改善人类的对齐程度。这些结果突显了定性行为差异，为人类-VLM对齐基准测试提供了基础见解。
英文摘要：
High text recognition performance does not guarantee that Vision-Language Models (VLMs) share human-like decision patterns when resolving ambiguity. We investigate this behavioral gap by directly comparing humans and VLMs using continuously interpolated Japanese character shapes generated via a $β$-VAE. We estimate decision boundaries in a single-character recognition (shape-only task) and evaluate whether VLM responses align with human judgments under shape in context (i.e., embedding an ambiguous character near the human decision boundary in word-level context). We find that human and VLM decision boundaries differ in the shape-only task, and that shape in context can improve human alignment in some conditions. These results highlight qualitative behavioral differences, offering foundational insights toward human--VLM alignment benchmarking.
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