The study focuses on the use of Large Language Models (LLMs) for fairness-aware classification tasks. It introduces a framework outlining fairness regulations aligned with various definitions and explores the configuration for in-context learning. Experiments show that GPT-4 delivers superior results in terms of accuracy and fairness compared to other models.
The content discusses the importance of assessing fairness in LLMs, highlighting potential biases and the need to mitigate them. It presents experiments conducted with different LLMs, showcasing their understanding of fairness concepts through responses to sensitive inquiries. The study aims to achieve fair outcomes by utilizing LLMs through in-context learning.
Key metrics and figures used to support the argument include accuracy rates, F1 scores, disparate impact values, true positive rates, false positive rates, predictive positive values, false omission rates, and overall accuracy equality metrics.
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by Garima Chhik... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18502.pdfDeeper Inquiries