ProText: A Benchmark Dataset for Measuring (Mis)gendering in Long-Form Texts
Apple Machine Learning Journal / 3/31/2026
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Key Points
- ProText is introduced as a benchmark dataset specifically designed to measure (mis)gendering behavior in long-form text settings.
- The paper (published March 2026) is positioned within fairness and NLP research, focusing on evaluating gender-related errors in generated or processed text.
- By targeting long-form documents, ProText aims to capture performance issues that may not appear in shorter text benchmarks.
- The publication provides an entry point (via its arXiv link) for researchers and practitioners to evaluate and compare systems on gendering robustness.
- The dataset is intended to support more rigorous fairness assessment for NLP models that generate or analyze extended natural-language content.
We introduce ProText, a dataset for measuring gendering and misgendering in stylistically diverse long-form English texts. ProText spans three dimensions: Theme nouns (names, occupations, titles, kinship terms), Theme category (stereotypically male, stereotypically female, gender-neutral/non-gendered), and Pronoun category (masculine, feminine, gender-neutral, none). The dataset is designed to probe (mis)gendering in text transformations such as summarization and rewrites using state-of-the-art Large Language Models, extending beyond traditional pronoun resolution benchmarks and beyond the…
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