Fairness in Large Language Models:A Tutorial

Abstract

Large Language Models (LLMs) have demonstrated remarkable success across various domains over the years. However, despite their promising performance on various real world tasks, most of these algorithms lack fairness considerations, potentially leading to discriminatory outcomes against marginalized demographic groups and individuals. Many recent publications have explored ways to mitigate bias in LLMs. Nevertheless, a comprehensive understanding of the root causes of bias, their effects, and possible limitations of LLMs from the perspective of fairness is still in its early stages. To bridge this gap, this tutorial provides a systematic overview of recent advances in fair LLMs, beginning with real-world case studies, followed by an analysis of bias causes. We then explore fairness concepts specific to LLMs, summarizing bias evaluation strategies and algorithms designed to promote fairness. Finally, we analyze bias in LLM datasets and discuss current research challenges and open questions in the field.

Authors

Zichong Wang, Avash Palikhe, Zhipeng Yin, Jiale Zhang and Wenbin Zhang

Website

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