Papers by Jonas F. Lotz

2 papers
Multilingual Pretraining for Pixel Language Models (2025.emnlp-main)

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Challenge: PIXEL-M4 model pretrains on four visually and linguistically diverse languages . previous work on pixel-based language models focused on monolingual pretraining on English data .
Approach: They propose a pixel-based language model that is pretrained on four visually diverse languages.
Outcome: The proposed model outperforms an English-only counterpart on non-Latin scripts on semantic and syntactic tasks.
Beyond Text Compression: Evaluating Tokenizers Across Scales (2025.acl-long)

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Challenge: Language models rely on tokenizers to convert text into machine-interpretable tokens, which shape the statistical patterns that language models learn to estimate.
Approach: They propose to use Zipf's law to measure tokenizer performance by combining several metrics to capture multiple aspects of tokenizer behavior.
Outcome: The proposed metrics correlate more strongly with downstream performance than text compression when modeling unseen languages.

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