Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings

• URL: http://arxiv.org/abs/2412.06134v3
• Date: Tue, 07 Oct 2025 05:04:25 GMT
• Title: Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings
• Authors: Zhao Liu, Tian Xie, Xueru Zhang,
• Abstract summary: We extend an existing dataset BBQ to Open-BBQ to evaluate the social bias of LLMs in open-ended settings.<n>We develop an evaluation process to detect biases from open-ended content by labeling sentences and paragraphs.<n>In order to solve this issue, we propose Composite Prompting, an In-context Learning (ICL) method combining structured examples with explicit chain-of-thought reasoning.
• Score: 18.318232025355524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current social bias benchmarks for Large Language Models (LLMs) primarily rely on predefined question formats like multiple-choice, limiting their ability to reflect the complexity and open-ended nature of real-world interactions. To close this gap, we extend an existing dataset BBQ (Parrish et al., 2022) to Open-BBQ, a comprehensive framework to evaluate the social bias of LLMs in open-ended settings by incorporating two additional question categories: fill-in-the-blank and short-answer. Since our new Open-BBQ dataset contains a lot of open-ended responses like sentences and paragraphs, we developed an evaluation process to detect biases from open-ended content by labeling sentences and paragraphs. In addition to this, we also found that existing debiasing methods, such as self-debiasing (Gallegos et al., 2024), have over-correction issues, which make the original correct answers incorrect. In order to solve this issue, we propose Composite Prompting, an In-context Learning (ICL) method combining structured examples with explicit chain-of-thought reasoning to form a unified instruction template for LLMs to explicitly identify content that needs debiasing. Experimental results show that the proposed method significantly reduces the bias for both GPT-3.5 and GPT-4o while maintaining high accuracy.

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