17 citations · 17 across the 6 of their papers we have counts for
6 papers
Beyond Text Compression: Evaluating Tokenizers Across Scales
Jonas F. Lotz, António V. Lopes, Stephan Peitz +2
The choice of tokenizer can profoundly impact language model performance, yet accessible and reliable evaluations of tokenizer quality remain an open challenge. Inspired by scaling…
Multilingual Pretraining for Pixel Language Models
Ilker Kesen, Jonas F. Lotz, Ingo Ziegler +2
Pixel language models operate directly on images of rendered text, eliminating the need for a fixed vocabulary. While these models have demonstrated strong capabilities for downstr…
Overcoming Vocabulary Constraints with Pixel-level Fallback
Jonas F. Lotz, Hendra Setiawan, Stephan Peitz +1
Subword tokenization requires balancing computational efficiency and vocabulary coverage, which often leads to suboptimal performance on languages and scripts not prioritized durin…
Text Rendering Strategies for Pixel Language Models
Jonas F. Lotz, Elizabeth Salesky, Phillip Rust +1
Pixel-based language models process text rendered as images, which allows them to handle any script, making them a promising approach to open vocabulary language modelling. However…
The Role of Data Curation in Image Captioning
Wenyan Li, Jonas F. Lotz, Chen Qiu +1
Image captioning models are typically trained by treating all samples equally, neglecting to account for mismatched or otherwise difficult data points. In contrast, recent work has…
Language Modelling with Pixels
Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello +3
Language models are defined over a finite set of inputs, which creates a vocabulary bottleneck when we attempt to scale the number of supported languages. Tackling this bottleneck…