"Im not Racist but...": Discovering Bias in the Internal Knowledge of
  Large Language Models
Abel Salinas Louis Penafiel Robert McCormack Fred Morstatter
Large language models (LLMs) have garnered significant attention for their
remarkable performance in a continuously expanding set of natural language
processing tasks. However, these models have been shown to harbor inherent
societal biases, or stereotypes, which can adversely affect their performance
in their many downstream applications. In this paper, we introduce a novel,
purely prompt-based approach to uncover hidden stereotypes within any arbitrary
LLM. Our approach dynamically generates a knowledge representation of internal
stereotypes, enabling the identification of biases encoded within the LLM's
internal knowledge. By illuminating the biases present in LLMs and offering a
systematic methodology for their analysis, our work contributes to advancing
transparency and promoting fairness in natural language processing systems.