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

arXiv: https://arxiv.org/abs/2301.00395 · HTML: https://mlsanity.com/paper/2301.00395/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018)
- [Learning Gender-Neutral Word Embeddings](https://mlsanity.com/paper/1809.01496/) (2018)
- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [Examining Gender Bias in Languages with Grammatical Gender](https://mlsanity.com/paper/1909.02224/) (2019)
- [Multi-label Categorization of Accounts of Sexism using a Neural Framework](https://mlsanity.com/paper/1910.04602/) (2019)
- [Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives](https://mlsanity.com/paper/2106.00181/) (2021)

## Built on by (most cited first, 19 of 19)

- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [Evaluating Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2310.19736/) (2023)
- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models](https://mlsanity.com/paper/2306.16244/) (2023)
- [GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models](https://mlsanity.com/paper/2406.13925/) (2024)
- [Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology](https://mlsanity.com/paper/2405.06346/) (2024)
- [''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text](https://mlsanity.com/paper/2310.17428/) (2023)
- [CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models](https://mlsanity.com/paper/2402.13109/) (2024)
- [OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety](https://mlsanity.com/paper/2403.12316/) (2024)
- [Implicit Bias in LLMs: A Survey](https://mlsanity.com/paper/2503.02776/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation](https://mlsanity.com/paper/2408.04378/) (2024)
- [Observing Micromotives and Macrobehavior of Large Language Models](https://mlsanity.com/paper/2412.10428/) (2024)
- [Detection, Classification, and Mitigation of Gender Bias in Large Language Models](https://mlsanity.com/paper/2506.12527/) (2025)
- [Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge](https://mlsanity.com/paper/2506.12574/) (2025)
- [From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing](https://mlsanity.com/paper/2509.07889/) (2025)
- [Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups](https://mlsanity.com/paper/2510.06974/) (2025)
- [Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengali](https://mlsanity.com/paper/2601.17764/) (2026)
- [MAAM: Anchor-Preserving Compression and Contextual Calibration for Chinese Discriminatory Language Detection](https://mlsanity.com/paper/2606.09114/) (2026)

## Abstract (arXiv)

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.