Mitigating Political Bias In Language Models Through Reinforced Calibration | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A collection of research papers on LLMs.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

Mitigating Political Bias In Language Models Through Reinforced Calibration

Ruibo Liu, Chenyan Jia, Jason Wei, Guangxuan Xu, Lili Wang, Soroush Vosoughi . Proceedings of the AAAI Conference on Artificial Intelligence 2021 – 51 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
AAAI Compositional Generalization Ethics & Fairness Evaluation Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Reinforcement Learning Tools Training Techniques

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.

Similar Work