Nicola Fanton


2024

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On Shortcuts and Biases: How Finetuned Language Models Distinguish Audience-Specific Instructions in Italian and English
Nicola Fanton | Michael Roth
Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Instructional texts for different audience groups can help to address specific needs, but at the same time run the risk of perpetrating biases. In this paper, we extend previous findings on disparate social norms and subtle stereotypes in wikiHow in two directions: We explore the use of fine-tuned language models to determine how audience-specific instructional texts can be distinguished and we transfer the methodology to another language, Italian, to identify cross-linguistic patterns. We find that language models mostly rely on group terms, gender markings, and attributes reinforcing stereotypes.

2023

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How-to Guides for Specific Audiences: A Corpus and Initial Findings
Nicola Fanton | Agnieszka Falenska | Michael Roth
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

Instructional texts for specific target groups should ideally take into account the prior knowledge and needs of the readers in order to guide them efficiently to their desired goals. However, targeting specific groups also carries the risk of reflecting disparate social norms and subtle stereotypes. In this paper, we investigate the extent to which how-to guides from one particular platform, wikiHow, differ in practice depending on the intended audience. We conduct two case studies in which we examine qualitative features of texts written for specific audiences. In a generalization study, we investigate which differences can also be systematically demonstrated using computational methods. The results of our studies show that guides from wikiHow, like other text genres, are subject to subtle biases. We aim to raise awareness of these inequalities as a first step to addressing them in future work.