I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs

Large Language Models (LLMs) perform strongly on many language tasks, but their capability in structurally constrained, accessibility-critical modalities such as Braille remains unclear.

We evaluate state-of-the-art LLMs on bidirectional Korean-Braille translation using a human-annotated dataset.

Despite expectations that multilingual, instruction-tuned models can generalize to Braille via text representations, we find consistently poor, unstable outputs and substantial disagreement with human judgments.

These results point to missing Braille-aware tokenization and weak alignment between Korean and Braille patterns.

In contrast, supervised fine-tuning of a small model (T5-small) on the same data yields large and stable gains over zero-shot and prompted LLM baselines across standard metrics (SacreBLEU, ChrF++, CER, BLEU, ROUGE-L, METEOR, CIDEr).

Our findings reveal a systematic limitation of current LLMs and demonstrate the effectiveness of modest task-specific supervision.