1 citations · 2 across the 7 of their papers we have counts for
7 papers
Low-Resource Preference Adaptation of LLMs via Activation-Based Label Propagation
Alessio Galatolo, Meriem Beloucif
Adapting large language models to user-specific preferences is often constrained by the cost of human annotation, making preference optimisation impractical in low-resource setting…
Lightweight Visual Reasoning for Socially-Aware Robots
Alessio Galatolo, Ronald Cumbal, Alexandros Rouchitsas +3
Robots operating in shared human environments must not only navigate, interact, and detect their surroundings, they must also interpret and respond to dynamic, and often unpredicta…
Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants
Alessio Galatolo, Luca Alberto Rappuoli, Katie Winkle +1
The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to…
Visualising Policy-Reward Interplay to Inform Zeroth-Order Preference Optimisation of Large Language Models
Alessio Galatolo, Zhenbang Dai, Katie Winkle +1
Fine-tuning Large Language Models (LLMs) with first-order methods like back-propagation is computationally intensive. Zeroth-Order (ZO) optimisation uses function evaluations inste…
DIEKAE: Difference Injection for Efficient Knowledge Augmentation and Editing of Large Language Models
Alessio Galatolo, Meriem Beloucif, Katie Winkle
Pretrained Language Models (PLMs) store extensive knowledge within their weights, enabling them to recall vast amount of information. However, relying on this parametric knowledge…
Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research
Eleonora Mancini, Ana Tanevska, Andrea Galassi +3
Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research de…
Using Early-Learning Regularization to Classify Real-World Noisy Data
Alessio Galatolo, Alfred Nilsson, Roderick Karlemstrand +1
The memorization problem is well-known in the field of computer vision. Liu et al. propose a technique called Early-Learning Regularization, which improves accuracy on the CIFAR da…