In this project, we want to explore the newly emerging field of prompt
engineering and apply it to the downstream task of detecting LM biases. More
concretely, we explore how to design prompts that can indicate 4 different
types of biases: (1) gender, (2) race, (3) sexual orientation, and (4)
religion-based. Within our project, we experiment with different manually
crafted prompts that can draw out the subtle biases that may be present in the
language model. We apply these prompts to multiple variations of popular and
well-recognized models: BERT, RoBERTa, and T5 to evaluate their biases. We
provide a comparative analysis of these models and assess them using a two-fold
method: use human judgment to decide whether model predictions are biased and
utilize model-level judgment (through further prompts) to understand if a model
can self-diagnose the biases of its own prediction.

在该项目中，我们探索了新兴的提示工程领域，并将其应用于检测语言模型偏见的下游任务。具体而言，我们研究如何设计能够指示 4 种不同类型偏见（性别、种族、性取向和基于宗教）的提示。我们在多个流行和公认的模型（BERT、RoBERTa 和 T5）的不同变体上应用这些提示来评估其偏见。我们提供了这些模型的比较分析，并采用两种方法进行评估：使用人的判断来决定模型预测是否存在偏见，并利用模型级别的判断（通过进一步的提示）来了解模型是否能够自我诊断其预测的偏见。