The rapid development of Large Language Models (LLMs) has called for robust benchmarks
to assess their abilities, track progress, and compare iterations. While existing benchmarks …

This paper provides a thorough review of recommendation methods from academic
literature, offering a taxonomy that classifies recommender systems (RSs) into categories …

Abstract Recent Large Language Models (LLMs) have shown impressive performance in
addressing complex aspects of human language. These models have also demonstrated …

The growing interest in Large Language Models (LLMs) has accelerated research efforts to
adapt these models for various languages. Despite this, pretraining LLMs from scratch for …

While Italian is a high-resource language, there are few Italian-native benchmarks to
evaluate generative Large Language Models (LLMs) in this language. This work presents …

Large Language Models (LLMs) have shown remarkable performance across various tasks,
yet significant disparities remain for non-English languages, and especially native African …

Adapting models to a language that was only partially present in the pre-training data
requires fine-tuning, which is expensive in terms of both data and computational resources …

This work presents a methodology for adapting an open Large Language Model (LLM) to
the Italian legal domain. We construct a legal document corpus from the Normattiva website …

The primary goal of Recommender Systems is to suggest the most suitable items to a user,
aligning them with the user's interests and needs. RSs are essential for modern e …

This paper illustrates the architecture and training of Unipa-GPT, a chatbot relying on a
Large Language Model, developed for assisting students in choosing a bachelor/master …