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

AmazUtah_NLP at SemEval-2024 Task 9: A MultiChoice Question Answering System for Commonsense Defying Reasoning


View a PDF of the paper titled Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings, by Zhao Liu and 2 other authors

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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.

Submission history

From: Zhao Liu [view email]
[v1]
Mon, 9 Dec 2024 01:29:47 UTC (119 KB)
[v2]
Sun, 19 Jan 2025 23:21:59 UTC (119 KB)



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