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CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation

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arxiv 2301.00395 v1 pith:NFQKXUSI submitted 2023-01-01 cs.CL cs.AIcs.CYcs.LG

Ge Zhang , Yizhi Li , Yaoyao Wu , Linyuan Zhang , Chenghua Lin , Jiayi Geng , Shi Wang , Jie Fu This is my paper · ORCID
classification cs.CLcs.AIcs.CYcs.LG
keywords biasgenderchinesecorpusmitigationcorgi-pmlanguagemodels
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As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient 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. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups

    cs.CL 2025-10 conditional novelty 5.0 of 10

    Chinese LLMs generate more positive continuations after 'we' prompts and more negative after 'they' prompts; the feminine 'they' intensifies negativity in several pretrained models.

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

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A Chinese gender-bias corpus and shared-task benchmark show detection and classification are feasible, while automatic mitigation remains weak.

  3. Detection, Classification, and Mitigation of Gender Bias in Large Language Models

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A Chinese gender-bias system using SFT, chain-of-thought, and DPO with GPT-4-generated preference pairs reports top validation scores and first place on all three NLPCC 2025 subtasks.

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