# The Impact of Visual Information in Chinese Characters: Evaluating Large Models' Ability to Recognize and Utilize Radicals

arXiv: https://arxiv.org/abs/2410.09013 · HTML: https://mlsanity.com/paper/2410.09013/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [Prompting Language Models for Linguistic Structure](https://mlsanity.com/paper/2211.07830/) (2022)
- [A Sub-Character Architecture for Korean Language Processing](https://mlsanity.com/paper/1707.06341/) (2017)

## Built on by (most cited first, 10 of 10)

- [EXECUTE: A Multilingual Benchmark for LLM Token Understanding](https://mlsanity.com/paper/2505.17784/) (2025)
- [Spelling-out is not Straightforward: LLMs' Capability of Tokenization from Token to Characters](https://mlsanity.com/paper/2506.10641/) (2025)
- [Visible Yet Unreadable: A Systematic Blind Spot of Vision Language Models Across Writing Systems](https://mlsanity.com/paper/2509.06996/) (2025)
- [Quantifying Phonosemantic Iconicity Distributionally in 6 Languages](https://mlsanity.com/paper/2510.14040/) (2025)
- [Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with B'ezier Curves](https://mlsanity.com/paper/2511.00076/) (2025)
- [Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors](https://mlsanity.com/paper/2601.05508/) (2026)
- [Phun-Bench: Evaluating LLMs on Phonological Understanding in Chinese](https://mlsanity.com/paper/2606.07300/) (2026)
- [One Image, No Tokens: A Controlled Study of Glyph-Based Chinese Language Modeling](https://mlsanity.com/paper/2607.03994/) (2026)
- [The First ChineseBabyLM Challenge: training data-efficient and cognitively plausible language models for Chinese](https://mlsanity.com/paper/2607.10745/) (2026)
- [CNM-BERT: A Drop-In Structural Embedding for Chinese Characters via Ideographic Description Sequences](https://mlsanity.com/paper/2608.05167/) (2026)

## Abstract (arXiv)

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.