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Phillip Rust

10 accepted papers

2025

Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users

Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos, Danae Sanchez Villegas, Phillip Rust, Ruchira Dhar +2
ACL 2025long

This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify adoption patterns and key challenges users face with such technologies. Despite a high adoption rate of these models, ou…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Multilingual Pretraining for Pixel Language Models

Ilker Kesen, Jonas F. Lotz, Ingo Ziegler, Phillip Rust, Desmond Elliott
EMNLP 2025

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 downstream cross-lingual transfer, multilingual pretraining remains underexplored. We introduce PIXEL-M4, a model pretrained on

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2025

Trick or Neat: Adversarial Ambiguity and Language Model Evaluation

Antonia Karamolegkou, Oliver Eberle, Phillip Rust, Carina Kauf, Anders Søgaard
ACL 2025finding

Detecting ambiguity is important for language understanding, including uncertainty estimation, humour detection, and processing garden path sentences. We assess language models’ sensitivity to ambiguity by introducing an adversarial ambiguity dataset that includes syntactic, lexical, and phonologica…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Towards Privacy-Aware Sign Language Translation at Scale

Phillip Rust, Bowen Shi, Skyler Wang, Necati Cihan Camgoz, Jean Maillard
ACL 2024long

A major impediment to the advancement of sign language translation (SLT) is data scarcity. Much of the sign language data currently available on the web cannot be used for training supervised models due to the lack of aligned captions. Furthermore, scaling SLT using large-scale web-scraped datasets…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2023

Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models

Phillip Rust, Anders Søgaard
ICML 2023poster

Language models such as mBERT, XLM-R, and BLOOM aim to achieve multilingual generalization or compression to facilitate transfer to a large number of (potentially unseen) languages. However, these models should ideally also be private, linguistically fair, and transparent, by relating their predicti…

Cited by 6Source ↗PDF ↗Scholar ↗
2023

Language Modelling with Pixels

Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, Desmond Elliott
ICLR 2023top-5%

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 results in a trade-off between what can be represented in the embedding matrix and computational issues in the output la…

Cited by 54Source ↗PDF ↗code ↗Scholar ↗
2023

PHD: Pixel-Based Language Modeling of Historical Documents

Nadav Borenstein, Phillip Rust, Desmond Elliott, Isabelle Augenstein
EMNLP 2023long main

The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involves converting them from images to text using OCR, a process that overlooks the potential benefits of treating them as ima…

Cited by 0Source ↗code ↗Scholar ↗
2023

Text Rendering Strategies for Pixel Language Models

Jonas F. Lotz, Elizabeth Salesky, Phillip Rust, Desmond Elliott
EMNLP 2023long main

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, recent approaches use text renderers that produce a large set of almost-equivalent input patches, which may prove sub-…

Cited by 23Source ↗Scholar ↗
2022

Challenges and Strategies in Cross-Cultural NLP

Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl +8
ACL 2022long

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by…

Cited by 58Source ↗PDF ↗Scholar ↗
2021

How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, Iryna Gurevych
ACL 2021long

In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained mo…

Cited by 270Source ↗PDF ↗code ↗Scholar ↗