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Sebastiano Vecellio Salto

2 accepted papers

2025

Job Unfair: An Investigation of Gender and Occupational Bias in Free-Form Text Completions by LLMs

EMNLP 2025

Disentangling how gender and occupations are encoded by LLMs is crucial to identify possible biases and prevent harms, especially given the widespread use of LLMs in sensitive domains such as human resources.In this work, we carry out an in-depth investigation of gender and occupational biases in En

2024

Delving into Qualitative Implications of Synthetic Data for Hate Speech Detection

EMNLP 2024main

The use of synthetic data for training models for a variety of NLP tasks is now widespread. However, previous work reports mixed results with regards to its effectiveness on highly subjective tasks such as hate speech detection. In this paper, we present an in-depth qualitative analysis of the poten…