测试大模型对汉字部件的视觉理解能力，发现其有限但可提升。
The Impact of Visual Information in Chinese Characters: Evaluating Large Models' Ability to Recognize and Utilize Radicals
- 构建中文字符视觉特征评估基准，涵盖部首、结构、笔画等
- 模型对部首等视觉信息掌握有限，但提示后性能有提升
- 在词性标注中加入部首信息可稳定提升效果，适合中文处理研究者
汉字的象形文字体系包含丰富的视觉特征，如表意或表音的部首。然而，现有大型语言模型（LLMs）与视觉-语言模型（VLMs）是否能通过提示利用这些子字符特征仍无研究。本文建立基准，评估模型对汉字部首、构字结构、笔画及笔画数的视觉理解能力。结果表明，无论是否提供字符图像，模型仅表现出有限但确实存在的视觉知识。为进一步激发模型使用部首的能力，我们在中文语言处理任务中引入部首提示，观察到词性标注性能持续提升，表明整合子字符信息具有增强中文处理的潜力。
原文摘要 · Abstract (English)
The glyphic writing system of Chinese incorporates information-rich visual features in each character, such as radicals that provide hints about meaning or pronunciation. However, there has been no investigation into whether contemporary Large Language Models (LLMs) and Vision-Language Models (VLMs) can harness these sub-character features in Chinese through prompting. In this study, we establish a benchmark to evaluate LLMs' and VLMs' understanding of visual elements in Chinese characters, including radicals, composition structures, strokes, and stroke counts. Our results reveal that models surprisingly exhibit some, but still limited, knowledge of the visual information, regardless of whether images of characters are provided. To incite models' ability to use radicals, we further experiment with incorporating radicals into the prompts for Chinese language processing (CLP) tasks. We observe consistent improvement in Part-Of-Speech tagging when providing additional information about radicals, suggesting the potential to enhance CLP by integrating sub-character information.
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