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Co²PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning

19 Oct 2023arXiv:2310.12490archive 2025-07-28

Xiangjue Dong, Ziwei Zhu, Zhuoer Wang, Maria Teleki, James Caverlee

Pre-trained Language Models are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-training corpora and even amplify biases in downstream applications. To address this challenge, we propose Co²PT, an efficient and effective debias-while-prompt tuning method for mitigating biases via counterfactual contrastive prompt tuning on downstream tasks. Our experiments conducted on three extrinsic bias benchmarks demonstrate the effectiveness of Co²PT on bias mitigation during the prompt tuning process and its adaptability to existing upstream debiased language models. These findings indicate the strength of Co²PT and provide promising avenues for further enhancement in bias mitigation on downstream tasks.

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