Investigating Gender Bias in Large Language Models for the Italian Language

Large Language Models (LLMs) are becoming increasingly flexible and reliable: the large pre-training phase enables them to capture a large number of real-world linguistic phenomena. However, pre-training on large amounts of data can also cause the representation of harmful biases. In this paper, we propose a method for identifying the presence of gender bias using a list of occupations characterized by a large imbalance between the number of male and female employees.