Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge

TL;DR

This paper introduces CORGI-PM, a Chinese corpus for gender bias detection and mitigation, and presents a shared task challenge to develop models that can identify, classify, and reduce gender bias in Chinese text.

Contribution

It provides a new annotated Chinese dataset for gender bias, along with a shared task framework to advance bias mitigation techniques in NLP.

Findings

Participating models show varying effectiveness in bias detection and mitigation.

The shared task results highlight the challenges in reducing gender bias in Chinese NLP.

Analysis suggests room for improvement in bias classification accuracy.

Abstract

As natural language processing for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques, such as pre-trained language models, suffer from biased corpus. This case becomes more obvious regarding those languages with less fairness-related computational linguistic resources, such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation (CORGI-PM), which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. It is worth noting that CORGI-PM contains 5.2k gender-biased sentences along with the corresponding bias-eliminated versions rewritten by human annotators. We pose three challenges as a shared task to automate the mitigation of textual gender bias, which requires the models to detect, classify, and mitigate textual gender bias. In the literature, we present the results and analysis for the teams participating this shared task in NLPCC 2025.