A word-as-image pixel language model trained with next-token prediction reports lower perplexity than a token-embedding LLaMA on noisy and non-Latin-script text, though its noise evaluation holds tokenization fixed.
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Enhancing Robustness of Autoregressive Language Models against Orthographic Attacks via Pixel-based Approach
A word-as-image pixel language model trained with next-token prediction reports lower perplexity than a token-embedding LLaMA on noisy and non-Latin-script text, though its noise evaluation holds tokenization fixed.