Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere. Despite this success, these models can learn, perpetuate, and amplify harmful social biases. In this paper, we present a comprehensive survey of bias evaluation and mitigation techniques for LLMs. We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing, defining distinct facets of harm and introducing several desiderata to operationalize fairness for LLMs. We then unify the literature by proposing three intuitive taxonomies, two for bias evaluation, namely metrics and datasets, and one for mitigation. Our first taxonomy of metrics for bias evaluation disambiguates the relationship between metrics and evaluation datasets, and organizes metrics by the different levels at which they operate in a model: embeddings, probabilities, and generated text. Our second taxonomy of datasets for bias evaluation categorizes datasets by their structure as counterfactual inputs or prompts, and identifies the targeted harms and social groups; we also release a consolidation of publicly-available datasets for improved access. Our third taxonomy of techniques for bias mitigation classifies methods by their intervention during pre-processing, in-training, intra-processing, and post-processing, with granular subcategories that elucidate research trends. Finally, we identify open problems and challenges for future work. Synthesizing a wide range of recent research, we aim to provide a clear guide of the existing literature that empowers researchers and practitioners to better understand and prevent the propagation of bias in LLMs.

翻译：大语言模型（LLMs）的快速发展使其能够处理、理解和生成类人文本，并日益融入触及我们社会领域的系统。尽管取得了成功，但这些模型可能学习、延续并放大有害的社会偏见。本文对LLMs的偏见评估与缓解技术进行了全面综述。我们首先整合、形式化并拓展了自然语言处理中社会偏见与公平性的概念，定义了危害的不同方面，并引入若干操作化LLMs公平性的准则。随后，我们通过提出三个直观的分类体系来统一相关文献：两个用于偏见评估（即度量和数据集），一个用于缓解。第一个度量分类体系澄清了度量与评估数据集之间的关系，并按度量在模型中运作的不同层次（嵌入层、概率层和生成文本层）进行组织。第二个数据集分类体系根据数据集结构（反事实输入或提示）进行归类，并识别了目标危害和社会群体；我们还整合了公开可用的数据集以提升可访问性。第三个偏见缓解技术分类体系根据方法在预处理、训练中、处理内和后处理阶段的干预方式进行分类，并包含细粒度子类别以阐明研究趋势。最后，我们指出了未来工作的开放问题和挑战。通过综合广泛的最新研究，我们旨在提供现有文献的清晰指南，使研究人员和实践者能够更好地理解并防止LLMs中偏见的传播。