  Large Language Models (LLMs) have shown powerful performance and development
prospects and are widely deployed in the real world. However, LLMs can capture
social biases from unprocessed training data and propagate the biases to
downstream tasks. Unfair LLM systems have undesirable social impacts and
potential harms. In this paper, we provide a comprehensive review of related
research on fairness in LLMs. Considering the influence of parameter magnitude
and training paradigm on research strategy, we divide existing fairness
research into oriented to medium-sized LLMs under pre-training and fine-tuning
paradigms and oriented to large-sized LLMs under prompting paradigms. First,
for medium-sized LLMs, we introduce evaluation metrics and debiasing methods
from the perspectives of intrinsic bias and extrinsic bias, respectively. Then,
for large-sized LLMs, we introduce recent fairness research, including fairness
evaluation, reasons for bias, and debiasing methods. Finally, we discuss and
provide insight on the challenges and future directions for the development of
fairness in LLMs.
