Beyond Clean Accuracy: The Practical Challenges and Benefits of Pixel-Based Language Models
Pixel-based language models process rendered text without relying on a fixed subword vocabulary, offering a promising approach to noisy and multilingual inputs. However, their practical value depends on whether they can be reliably adapted to downstream tasks. To this end, we systematically examine this question through fine-tuning behavior, typographical robustness, and zero-shot crosslingual transfer. Under our tested settings, pixel-based models require longer training and broader labelled-data coverage than text-based models, while delayed learning and learning-rate-dependent failures make adaptation less reliable. Our analyses further suggest that task-relevant information is less re...
| Authors | Haowen Tang, Wei Xia, Raoyuan Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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