Fairness in Large Language Models in Three Hours

TL;DR

This paper provides a comprehensive overview of fairness issues in large language models, covering bias causes, evaluation strategies, mitigation algorithms, and resources, highlighting current challenges and open questions in the field.

Contribution

It offers a systematic survey of recent advances in fair LLMs, including case studies, bias analysis, evaluation methods, and resource compilation, addressing unique fairness challenges in LLMs.

Findings

Bias causes in LLMs are diverse and complex.

Evaluation tools and datasets for bias assessment are available.

Current algorithms show promise but face open challenges.

Abstract

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}.