评测意大利语无性别重写模型，发现开源模型表现更优且可小型化。
Gender-Neutral Rewriting in Italian: Models, Approaches, and Trade-offs
- 构建双维度评估框架，同时衡量无性别化与语义保真度。
- 微调后模型性能媲美大模型，参数量仅为几分之一。
- 揭示训练数据优化中无性别与语义保留的权衡问题。
无性别重写（GNR）旨在消除文本中不必要的性别标识，同时保持原意，这对意大利语等具有语法性别的语言尤为挑战。本文首次系统评估了当前最先进的大语言模型（LLMs）在意大利语GNR任务上的表现，提出一个二维评估框架，兼顾中立性与语义保真度。我们对比了多种大模型的少样本提示策略，对选定模型进行微调，并应用针对性清洗提升任务相关性。结果表明，开源权重模型优于唯一现有专用意大利语GNR模型；而我们的微调模型在性能上可匹配甚至超越最佳开源模型，但仅需其极小参数量。最后，我们讨论了训练数据优化中追求无性别化与语义保留之间的权衡。
原文摘要 · Abstract (English)
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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