Papers by Pietro Palombini
Adaptive Data Collection for Latin-American Community-sourced Evaluation of Stereotypes (LACES) (2026.findings-acl)
Copied to clipboard
Guido Ivetta, Pietro Palombini, Sofía Martinelli, Marcos J Gomez, M Emilia Echeveste, Sunipa Dev, Vinodkumar Prabhakaran, Luciana Benotti
| Challenge: | a geo-cultural gap in NLP evaluation hinders evaluation of societal biases . authors propose a new method to collect stereotypes from large language models . |
| Approach: | They propose a new method that integrates sourcing and validation of existing data into a single workflow. |
| Outcome: | The proposed method improves LACES by integrating new stereotype entries and validation of existing data. |
HESEIA: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in Latin America (2025.emnlp-main)
Copied to clipboard
Guido Ivetta, Marcos J Gomez, Sofía Martinelli, Pietro Palombini, M Emilia Echeveste, Nair Carolina Mazzeo, Beatriz Busaniche, Luciana Benotti
| Challenge: | a dataset of 46,499 sentences created in a professional development course captures intersectional biases across multiple demographic axes and school subjects. |
| Approach: | They present a large-scale dataset of 46,499 sentences created in a professional development course . they show that the dataset contains more stereotypes unrecognized by current LLMs . |
| Outcome: | The proposed dataset captures intersectional biases across multiple demographic axes and school subjects. |