Mitigating Political Bias in Language Models Through Reinforced Calibration
Abstract
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political bias in GPT-2 generation and propose a reinforcement learning (RL) framework for mitigating political biases in generated text. By using rewards from word embeddings or a classifier, our RL framework guides debiased generation without having access to the training data or requiring the model to be retrained. In empirical experiments on three attributes sensitive to political bias (gender, location, and topic), our methods reduced bias according to both our metrics and human evaluation, while maintaining readability and semantic coherence.
Cite
Text
Liu et al. "Mitigating Political Bias in Language Models Through Reinforced Calibration." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17744Markdown
[Liu et al. "Mitigating Political Bias in Language Models Through Reinforced Calibration." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/liu2021aaai-mitigating/) doi:10.1609/AAAI.V35I17.17744BibTeX
@inproceedings{liu2021aaai-mitigating,
title = {{Mitigating Political Bias in Language Models Through Reinforced Calibration}},
author = {Liu, Ruibo and Jia, Chenyan and Wei, Jason and Xu, Guangxuan and Wang, Lili and Vosoughi, Soroush},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {14857-14866},
doi = {10.1609/AAAI.V35I17.17744},
url = {https://mlanthology.org/aaai/2021/liu2021aaai-mitigating/}
}