# BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks

arXiv: https://arxiv.org/abs/2510.18288 · HTML: https://mlsanity.com/paper/2510.18288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [LawLLM: Law Large Language Model for the US Legal System](https://mlsanity.com/paper/2407.21065/) (2024)
- [MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation](https://mlsanity.com/paper/1912.05467/) (2019)
- [DEEP: DEnoising Entity Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2111.07393/) (2021)
- [The University of Edinburgh's Submissions to the WMT19 News Translation Task](https://mlsanity.com/paper/1907.05854/) (2019)
- [Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning](https://mlsanity.com/paper/2010.09046/) (2020)

## Built on by (most cited first, 2 of 2)

- [I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs](https://mlsanity.com/paper/2607.11893/) (2026)
- [BrailleBench: Investigating Multi-Criteria Braille Comprehension in Large Language Models](https://mlsanity.com/paper/2608.27268/) (2026)

## Abstract (arXiv)

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.