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arXiv · 2509.13480

Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offs

Andrea Piergentili·Beatrice Savoldi·Matteo Negri·Luisa Bentivogli
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Abstract

Gender-neutral rewriting (GNR) aims to reformulate text to eliminate unnecessary gender specifications while preserving meaning, a particularly challenging task in grammatical-gender languages like Italian. In this work, we conduct the first systematic evaluation of state-of-the-art large language models (LLMs) for Italian GNR, introducing a two-dimensional framework that measures both neutrality and semantic fidelity to the input. We compare few-shot prompting across multiple LLMs, fine-tune selected models, and apply targeted cleaning to boost task relevance. Our findings show that open-weight LLMs outperform the only existing model dedicated to GNR in Italian, whereas our fine-tuned models match or exceed the best open-weight LLM's performance at a fraction of its size. Finally, we discuss the trade-off between optimizing the training data for neutrality and meaning preservation.

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Andrea Piergentili, Beatrice Savoldi, Matteo Negri, Luisa Bentivogli. 2025-09-16. Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offs. https://arxiv.org/abs/2509.13480

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