Fairness in Large Language Models in Three Hours
It tackles fairness issues in LLMs for researchers and practitioners, but it is incremental as it compiles and reviews existing literature rather than introducing new methods.
This tutorial addresses the problem of fairness in large language models (LLMs), which can lead to discriminatory outcomes, by providing a systematic overview of recent advances, including case studies, bias analysis, evaluation strategies, and fairness algorithms, without reporting specific numerical results.
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}.