The article discusses how advanced computer models can pick up biases from their training data, affecting their performance in real-world situations. The researchers suggest a method to train these models to be less biased and better at handling new information. By using a separate model to identify and adjust for biases, the main model can focus on learning from challenging examples. Testing on different datasets showed that this approach improved the models' performance significantly, making them more reliable in diverse scenarios.