The article discusses how advanced language models can struggle with biased data, leading to poor performance in real-world situations. The researchers suggest two strategies to train models that are less affected by biases and perform better on different types of data. By using bias-only models to identify and adjust for biases during training, the main models become more robust and accurate. Testing on various datasets shows that these methods significantly improve model performance and adaptability to new information.