Towards Auditing Large Language Models: Improving Text-based Stereotype
  Detection
Wu Zekun Sahan Bulathwela Adriano Soares Koshiyama
Large Language Models (LLM) have made significant advances in the recent past
becoming more mainstream in Artificial Intelligence (AI) enabled human-facing
applications. However, LLMs often generate stereotypical output inherited from
historical data, amplifying societal biases and raising ethical concerns. This
work introduces i) the Multi-Grain Stereotype Dataset, which includes 52,751
instances of gender, race, profession and religion stereotypic text and ii) a
novel stereotype classifier for English text. We design several experiments to
rigorously test the proposed model trained on the novel dataset. Our
experiments show that training the model in a multi-class setting can
outperform the one-vs-all binary counterpart. Consistent feature importance
signals from different eXplainable AI tools demonstrate that the new model
exploits relevant text features. We utilise the newly created model to assess
the stereotypic behaviour of the popular GPT family of models and observe the
reduction of bias over time. In summary, our work establishes a robust and
practical framework for auditing and evaluating the stereotypic bias in LLM.