7 citations · 10 across the 8 of their papers we have counts for
10 papers
Aloe-Vision: Robust Vision-Language Models for Healthcare
Jaume Guasch-Martí, Enrique Lopez-Cuena, Martín Suárez-Fernández +3
Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinical and biomedical applications.…
Language Models Can Explain Visual Features via Steering
Javier Ferrando, Enrique Lopez-Cuena, Pablo Agustin Martin-Torres +3
Sparse Autoencoders uncover thousands of features in vision models, yet explaining these features without requiring human intervention remains an open challenge. While previous wor…
Exploring SAIG Methods for an Objective Evaluation of XAI
Miquel Miró-Nicolau, Gabriel Moyà-Alcover, Anna Arias-Duart
The evaluation of eXplainable Artificial Intelligence (XAI) methods is a rapidly growing field, characterized by a wide variety of approaches. This diversity highlights the complex…
Bias by Design? How Data Practices Shape Fairness in AI Healthcare Systems
Anna Arias-Duart, Maria Eugenia Cardello, Atia Cortés
Artificial intelligence (AI) holds great promise for transforming healthcare. However, despite significant advances, the integration of AI solutions into real-world clinical practi…
The Aloe Family Recipe for Open and Specialized Healthcare LLMs
Dario Garcia-Gasulla, Jordi Bayarri-Planas, Ashwin Kumar Gururajan +10
Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public interest. This work contributes…
Efficient Safety Retrofitting Against Jailbreaking for LLMs
Dario Garcia-Gasulla, Adrian Tormos, Anna Arias-Duart +4
Direct Preference Optimization (DPO) is an efficient alignment technique that steers LLMs towards preferable outputs by training on preference data, bypassing the need for explicit…
Automatic Evaluation of Healthcare LLMs Beyond Question-Answering
Anna Arias-Duart, Pablo Agustin Martin-Torres, Daniel Hinjos +7
Current Large Language Models (LLMs) benchmarks are often based on open-ended or close-ended QA evaluations, avoiding the requirement of human labor. Close-ended measurements evalu…
Aloe: A Family of Fine-tuned Open Healthcare LLMs
Ashwin Kumar Gururajan, Enrique Lopez-Cuena, Jordi Bayarri-Planas +10
As the capabilities of Large Language Models (LLMs) in healthcare and medicine continue to advance, there is a growing need for competitive open-source models that can safeguard pu…
Focus! Rating XAI Methods and Finding Biases
Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla +1
AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature;…
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla +4
In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experi…