Fairness in Large Language Models: A Taxonomic Survey
Zhibo Chu Zichong Wang Wenbin Zhang
Large Language Models (LLMs) have demonstrated remarkable success across
various domains. However, despite their promising performance in numerous
real-world applications, most of these algorithms lack fairness considerations.
Consequently, they may lead to discriminatory outcomes against certain
communities, particularly marginalized populations, prompting extensive study
in fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in
traditional machine learning, entails exclusive backgrounds, taxonomies, and
fulfillment techniques. To this end, this survey presents a comprehensive
overview of recent advances in the existing literature concerning fair LLMs.
Specifically, a brief introduction to LLMs is provided, followed by an analysis
of factors contributing to bias in LLMs. Additionally, the concept of fairness
in LLMs is discussed categorically, summarizing metrics for evaluating bias in
LLMs and existing algorithms for promoting fairness. Furthermore, resources for
evaluating bias in LLMs, including toolkits and datasets, are summarized.
Finally, existing research challenges and open questions are discussed.