ADEPT: A DEbiasing PrompT Framework

Understand

Several works have proven that finetuning is an applicable approach for debiasing contextualized word embeddings.

• Similarly, discrete prompts with semantic meanings have shown to be effective in debiasing tasks.
• With unfixed mathematical representation at the token level, continuous prompts usually surpass discrete ones at providing a pre-trained language model (PLM) with additional task-specific information.
• Despite this, relatively few efforts have been made to debias PLMs by prompt tuning with continuous prompts compared to its discrete counterpart.