Detecting Natural Language Biases with Prompt-based Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 11 Sep 2023.
Builds on
25 of 34 references.
References · 25
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13 more
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