New framework boosts AI performance by tackling biases in training data.
The article explores how to make question-answering models better at handling different types of biases in data. By considering multiple biases at once during training, the models can learn more general knowledge and perform well on various types of questions. The researchers developed a method that assigns weights to examples based on their biases, helping the model rely less on biased data. They tested this method on question-answering tasks with data from different areas and found it improved performance compared to other methods.
