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

• URL: http://arxiv.org/abs/2506.12527v1
• Date: Sat, 14 Jun 2025 14:53:25 GMT
• Title: Detection, Classification, and Mitigation of Gender Bias in Large Language Models
• Authors: Xiaoqing Cheng, Hongying Zan, Lulu Kong, Jinwang Song, Min Peng,
• Abstract summary: We investigate how to enhance the capabilities of large language models (LLMs) in gender bias detection, classification, and mitigation.<n>We adopt reinforcement learning, chain-of-thoughts reasoning, and supervised fine-tuning to handle different Subtasks.<n>Our approach ranked first across all three subtasks of the NLPCC 2025 Shared Task 7.
• Score: 6.762310697831219
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid development of large language models (LLMs), they have significantly improved efficiency across a wide range of domains. However, recent studies have revealed that LLMs often exhibit gender bias, leading to serious social implications. Detecting, classifying, and mitigating gender bias in LLMs has therefore become a critical research focus. In the NLPCC 2025 Shared Task 7: Chinese Corpus for Gender Bias Detection, Classification and Mitigation Challenge, we investigate how to enhance the capabilities of LLMs in gender bias detection, classification, and mitigation. We adopt reinforcement learning, chain-of-thoughts (CoT) reasoning, and supervised fine-tuning to handle different Subtasks. Specifically, for Subtasks 1 and 2, we leverage the internal reasoning capabilities of LLMs to guide multi-step thinking in a staged manner, which simplifies complex biased queries and improves response accuracy. For Subtask 3, we employ a reinforcement learning-based approach, annotating a preference dataset using GPT-4. We then apply Direct Preference Optimization (DPO) to mitigate gender bias by introducing a loss function that explicitly favors less biased completions over biased ones. Our approach ranked first across all three subtasks of the NLPCC 2025 Shared Task 7.

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