67 citations · 80 across the 18 of their papers we have counts for
19 papers
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…
Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Dual-encoder Vision-Language Models (VLMs) such as CLIP are often characterized as bag-of-words systems due to their poor performance on compositional benchmarks. We argue that thi…
Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Jon Irureta, Gorka Azkune, Jon Imaz +2
Vertical Federated Learning (VFL) has emerged as a critical paradigm for collaborative model training in privacy-sensitive domains such as finance and healthcare. However, most exi…
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…
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…
Adding simple structure at inference improves Vision-Language Compositionality
Imanol Miranda, Ander Salaberria, Eneko Agirre +1
Dual encoder Vision-Language Models (VLM) such as CLIP are widely used for image-text retrieval tasks. However, those models struggle with compositionality, showing a bag-of-words-…
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…
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…
Unsupervised Domain Adaption for Neural Information Retrieval
Carlos Dominguez, Jon Ander Campos, Eneko Agirre +1
Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-b…
Do Multilingual Language Models Think Better in English?
Julen Etxaniz, Gorka Azkune, Aitor Soroa +2
Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machin…
Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle +2
Integrating outside knowledge for reasoning in visio-linguistic tasks such as visual question answering (VQA) is an open problem. Given that pretrained language models have been sh…
Inferring spatial relations from textual descriptions of images
Aitzol Elu, Gorka Azkune, Oier Lopez de Lacalle +3
Generating an image from its textual description requires both a certain level of language understanding and common sense knowledge about the spatial relations of the physical enti…
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
Jon Ander Campos, Kyunghyun Cho, Arantxa Otegi +3
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In…
Evaluating Multimodal Representations on Visual Semantic Textual Similarity
Oier Lopez de Lacalle, Ander Salaberria, Aitor Soroa +2
The combination of visual and textual representations has produced excellent results in tasks such as image captioning and visual question answering, but the inference capabilities…