Abstract
Pre-trained language models (PLMs) play a crucial role in various applications, including sensitive domains such as the hiring process. However, extensive research has unveiled that these models tend to replicate social biases present in their pre-training data, raising ethical concerns. In this study, we propose the TagDebias method, which proposes debiasing a dataset using type tags. It then proceeds to fine-tune PLMs on this debiased dataset. Experiments show that our proposed TagDebias model, when applied to a ranking task, exhibits significant improvements in bias scores.- Anthology ID:
- 2024.findings-naacl.101
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1553–1567
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.101
- DOI:
- Cite (ACL):
- Mehrnaz Moslemi and Amal Zouaq. 2024. TagDebias: Entity and Concept Tagging for Social Bias Mitigation in Pretrained Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1553–1567, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- TagDebias: Entity and Concept Tagging for Social Bias Mitigation in Pretrained Language Models (Moslemi & Zouaq, Findings 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.findings-naacl.101.pdf