@inproceedings{huang-etal-2025-braillellm,
    title = "{B}raille{LLM}: {B}raille Instruction Tuning with Large Language Models for {B}raille Domain Tasks",
    author = "Huang, Tianyuan  and
      Zhu, Zepeng  and
      Xing, Hangdi  and
      Shao, Zirui  and
      Yu, Zhi  and
      Yang, Chaoxiong  and
      He, Jiaxian  and
      Liu, Xiaozhong  and
      Bu, Jiajun",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1454/",
    pages = "28589--28600",
    ISBN = "979-8-89176-332-6",
    abstract = "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."
}