Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models

TL;DR

This paper investigates the linguistic and visual capabilities of pixel-based language models, revealing a gap between visual and linguistic understanding and how training strategies influence their learning process.

Contribution

It provides a comprehensive analysis of PIXEL's layered representations, highlighting the distinction between visual and linguistic features and the impact of rendering strategies.

Findings

Lower layers capture superficial visual features

Higher layers learn syntactic and semantic abstractions

Orthographic constraints can improve early surface-level learning

Abstract

Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script. PIXEL, a canonical example of such a model, is a vision transformer that has been pre-trained on rendered text. While PIXEL has shown promising cross-script transfer abilities and robustness to orthographic perturbations, it falls short of outperforming monolingual subword counterparts like BERT in most other contexts. This discrepancy raises questions about the amount of linguistic knowledge learnt by these models and whether their performance in language tasks stems more from their visual capabilities than their linguistic ones. To explore this, we probe PIXEL using a variety of linguistic and visual tasks to assess its position on the vision-to-language spectrum. Our findings reveal a substantial gap between the model's visual and linguistic understanding. The lower layers of PIXEL predominantly capture superficial visual features, whereas the higher layers gradually learn more syntactic and semantic abstractions. Additionally, we examine variants of PIXEL trained with different text rendering strategies, discovering that introducing certain orthographic constraints at the input level can facilitate earlier learning of surface-level features. With this study, we hope to provide insights that aid the further development of pixel-based language models.