5 papers
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales +19
Children acquire language grounding with remarkable robustness from limited visuo-linguistic input in ways that surpass today's best large multimodal models. Recent research sugges…
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation
Mahi Luthra, Jiayi Shen, Maxime Poli +14
Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-super…
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…
Trick or Neat: Adversarial Ambiguity and Language Model Evaluation
Antonia Karamolegkou, Oliver Eberle, Phillip Rust +2
Detecting ambiguity is important for language understanding, including uncertainty estimation, humour detection, and processing garden path sentences. We assess language models' se…
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users
Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos +5
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…