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.

本研究介绍了一个多维度刻板印象数据集以及英文文本的新型刻板印象分类器，并通过多类别训练模型在多种可解释 AI 工具下展示出较好的性能，利用该模型评估了流行的 GPT 模型系列的刻板印象行为，并观察到刻板印象的减少，从而为 LLM 的刻板印象偏见审计和评估建立了一个稳健而实用的框架。