Mitigating Political Bias in Language Models Through Reinforced Calibration
This addresses the problem of political bias in language models for real-world deployment, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled political bias in GPT-2 generation by proposing a reinforcement learning framework that uses rewards from word embeddings or a classifier to guide debiased text generation, reducing bias on sensitive attributes like gender, location, and topic while maintaining readability and coherence.
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.