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}.

本文探讨大型语言模型（LLMs）中的公平性问题，尤其是它们对边缘化群体可能产生的歧视性结果。通过系统回顾最近的研究进展，文章提供了对偏见原因的分析，以及评估和促进公平性的方法，旨在提升LLMs的应用公平性并解决当前研究中的挑战。 

大型语言模型的公平性研究：三小时概览