  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.
