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

• URL: http://arxiv.org/abs/2412.06134v2
• Date: Sun, 19 Jan 2025 23:21:59 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 BBQ dataset by incorporating fill-in-the-blank and short-answer question types. Our finding reveals that LLMs produce responses that are more biased against certain protected attributes, like age and socio-economic status. Our debiasing approach combined zero-shot, few-shot, and chain-of-thought could significantly reduce the level of bias to almost 0.
• Score: 13.686732204665738
• License:
• Abstract: Current social bias benchmarks for Large Language Models (LLMs) primarily rely on pre-defined question formats like multiple-choice, limiting their ability to reflect the complexity and open-ended nature of real-world interactions. To address this gap, we extend an existing BBQ dataset introduced by incorporating fill-in-the-blank and short-answer question types, designed to evaluate biases in an open-ended setting. Our finding reveals that LLMs tend to produce responses that are more biased against certain protected attributes, like age and socio-economic status. On the other hand, these biased outputs produced by LLMs can serve as valuable contexts and chains of thought for debiasing. Our debiasing approach combined zero-shot, few-shot, and chain-of-thought could significantly reduce the level of bias to almost 0. We open-source our evaluation and debiasing code hoping to encourage further measurements and mitigation of bias and stereotype in LLMs.

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