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.

翻译：大型语言模型（LLMs）展现出强大的性能与发展前景，并已广泛部署于现实世界中。然而，LLMs可能从未经处理的训练数据中捕获社会偏见，并将这些偏见传播至下游任务。不公平的LLM系统会带来不良的社会影响和潜在危害。本文对LLMs公平性相关研究进行了全面综述。考虑到参数规模与训练范式对研究策略的影响，我们将现有公平性研究划分为面向预训练与微调范式下的中型LLMs，以及面向提示范式下的大型LLMs。首先，针对中型LLMs，我们从内在偏见与外在偏见两个角度分别介绍了评估指标与去偏方法。其次，针对大型LLMs，我们介绍了近期公平性研究，包括公平性评估、偏见成因及去偏方法。最后，我们探讨并深入分析了LLMs公平性发展面临的挑战与未来方向。