Papers with pixel-based language models

3 papers
PHD: Pixel-Based Language Modeling of Historical Documents (2023.emnlp-main)

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Challenge: Recent years have seen a boom in efforts to digitise historical documents in numerous languages and sources, leading to a transformation in the way historians work.
Approach: They propose a method for generating synthetic scans to resemble real historical documents by pre-training a model to reconstruct masked patches instead of predicting token distributions.
Outcome: The proposed model can reconstruct masked patches and understand language well.
Autoregressive Pre-Training on Pixels and Texts (2024.emnlp-main)

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Challenge: pixel-based language modeling integrates visual and textual data to improve performance of language models.
Approach: They propose a method that integrates visual and textual data into an autoregressive framework.
Outcome: The proposed method improves performance of pixel-based language models by incorporating visual and textual data.
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models (2024.emnlp-main)

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Challenge: PIXEL is a vision transformer that has been pre-trained on rendered text . however, it is not able to outperform monolingual subwords like BERT .
Approach: They propose to use PIXEL as a vision transformer to train on rendered text to explore the gap between its visual and linguistic understanding.
Outcome: The proposed model outperforms monolingual subword models in most other contexts, but it lacks the linguistic knowledge to perform in language tasks.

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