This repository accompanies the paper:
Aditya Tomar, Rudra Murthy, and Pushpak Bhattacharyya. 2025. Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17304–17317, Vienna, Austria. Association for Computational Linguistics.
Citation
@inproceedings{tomar-etal-2025-stereotype,
title = "Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach",
author = "Tomar, Aditya and
Murthy, Rudra and
Bhattacharyya, Pushpak",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.889/",
doi = "10.18653/v1/2025.findings-acl.889",
pages = "17304--17317",
ISBN = "979-8-89176-256-5",
abstract = "Bias and stereotypes in language models can cause harm, especially in sensitive areas like content moderation and decision-making. This paper addresses bias and stereotype detection by exploring how jointly learning these tasks enhances model performance. We introduce StereoBias, a unique dataset labeled for bias and stereotype detection across five categories: religion, gender, socio-economic status, race, profession, and others, enabling a deeper study of their relationship. Our experiments compare encoder-only models and fine-tuned decoder-only models using QLoRA. While encoder-only models perform well, decoder-only models also show competitive results. Crucially, joint training on bias and stereotype detection significantly improves bias detection compared to training them separately. Additional experiments with sentiment analysis confirm that the improvements stem from the connection between bias and stereotypes, not multi-task learning alone. These findings highlight the value of leveraging stereotype information to build fairer and more effective AI systems."
}