67 citations · 80 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Existing Vision-Language Compositionality (VLC) benchmarks like SugarCrepe are formulated as image-to-text retrieval problems, where, given an image, the models need to select betw…
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…
When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
Tiziano Labruna, Jon Ander Campos, Gorka Azkune
In this paper, we demonstrate how Large Language Models (LLMs) can effectively learn to use an off-the-shelf information retrieval (IR) system specifically when additional context…
Grounding Spatial Relations in Text-Only Language Models
Gorka Azkune, Ander Salaberria, Eneko Agirre
This paper shows that text-only Language Models (LM) can learn to ground spatial relations like "left of" or "below" if they are provided with explicit location information of obje…
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle +3
Existing work has observed that current text-to-image systems do not accurately reflect explicit spatial relations between objects such as 'left of' or 'below'. We hypothesize that…