[Submitted on 7 May 2026]
Title:I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs
View a PDF of the paper titled I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs, by Abdullah Abdullah
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Abstract: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.
Comments: Accepted at the LTEDI Workshop at ACL 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11893 [cs.CL]
(or arXiv:2607.11893v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.11893
arXiv-issued DOI via DataCite
Submission history
From: Abdullah Abdullah [view email] [v1] Thu, 7 May 2026 03:29:17 UTC (584 KB)
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