  Large Language Models (LLMs) have demonstrated remarkable success across
various domains but often lack fairness considerations, potentially leading to
discriminatory outcomes against marginalized populations. Unlike fairness in
traditional machine learning, fairness in LLMs involves unique backgrounds,
taxonomies, and fulfillment techniques. This tutorial provides a systematic
overview of recent advances in the literature concerning fair LLMs, beginning
with real-world case studies to introduce LLMs, followed by an analysis of bias
causes therein. The concept of fairness in LLMs is then explored, summarizing
the strategies for evaluating bias and the algorithms designed to promote
fairness. Additionally, resources for assessing bias in LLMs, including
toolkits and datasets, are compiled, and current research challenges and open
questions in the field are discussed. The repository is available at
\url{https://github.com/LavinWong/Fairness-in-Large-Language-Models}.
