8 papers · 1 filter
Agreement in Representation Space for Open-Ended Self-Consistency
Paula Ontalvilla, Gorka Azkune, Aitor Ormazabal
Self-consistency improves LLM reasoning by sampling multiple outputs and selecting the most consistent answer, but existing formulations largely rely on exact matching and therefor…
Multimodal LLMs Do Not Compose Skills Optimally Across Modalities
Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
Skill composition is the ability to combine previously learned skills to solve new tasks. As neural networks acquire increasingly complex skills during their pretraining, it is not…
Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque
Lukas Arana, Julen Etxaniz, Ander Salaberria +1
Current Multimodal Large Language Models exhibit very strong performance for several demanding tasks. While commercial MLLMs deliver acceptable performance in low-resource language…
Vision-Language Models Struggle to Align Entities across Modalities
Iñigo Alonso, Gorka Azkune, Ander Salaberria +2
Cross-modal entity linking refers to the ability to align entities and their attributes across different modalities. While cross-modal entity linking is a fundamental skill needed…
Improving the Efficiency of Visually Augmented Language Models
Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune
Despite the impressive performance of autoregressive Language Models (LM) it has been shown that due to reporting bias, LMs lack visual knowledge, i.e. they do not know much about…
BertaQA: How Much Do Language Models Know About Local Culture?
Julen Etxaniz, Gorka Azkune, Aitor Soroa +2
Large Language Models (LLMs) exhibit extensive knowledge about the world, but most evaluations have been limited to global or anglocentric subjects. This raises the question of how…