@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks
%A Huang, Tianyuan
%A Zhu, Zepeng
%A Xing, Hangdi
%A Shao, Zirui
%A Yu, Zhi
%A Yang, Chaoxiong
%A He, Jiaxian
%A Liu, Xiaozhong
%A Bu, Jiajun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F huang-etal-2025-braillellm
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1454/
%P 28589-28600
Markdown (Informal)
[BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks](https://aclanthology.org/2025.emnlp-main.1454/) (Huang et al., EMNLP 2025)
ACL
- Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, and Jiajun Bu. 2025. BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 28589–28600, Suzhou, China. Association for Computational Linguistics.