Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarcity and ambiguities in mixed-text contexts. We construct English and Chinese Braille Mixed Datasets (EBMD/CBMD) with mathematical formulas to support diverse Braille domain research, and propose a syntax tree-based augmentation method tailored for Braille data. To address the underperformance of traditional fine-tuning methods in Braille-related tasks, we investigate Braille Knowledge-Based Fine-Tuning (BKFT), which reduces the learning difficulty of Braille contextual features. BrailleLLM employs BKFT via instruction tuning to achieve unified Braille translation, formula-to-Braille conversion, and mixed-text translation. Experiments demonstrate that BKFT achieves significant performance improvements over conventional fine-tuning in Braille translation scenarios. Our open-sourced datasets and methodologies establish a foundation for low-resource multilingual Braille research.
@article{arxiv.2510.18288,
title = {BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks},
author = {Tianyuan Huang and Zepeng Zhu and Hangdi Xing and Zirui Shao and Zhi Yu and Chaoxiong Yang and Jiaxian He and Xiaozhong Liu and Jiajun Bu},
journal= {arXiv preprint arXiv:2510.18288},
year = {2025}
}