Bias and Fairness in Large Language Models: A Survey

Understand

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.