A team is training a model to predict user preferences between two generated text responses. The training objective is to minimize the average loss calculated over a collected dataset of preferences. However, the data collection was flawed, resulting in a dataset that primarily contains preferences from a very specific, non-representative group of users. What is the most significant risk of using the average loss on this particular dataset as the primary metric for training the model?

From Theory to Practice: Expected vs. Empirical Loss

If a dataset used for training a preference model is extremely large, the average loss calculated over this dataset is guaranteed to be a highly accurate approximation of the theoretical loss over the entire data distribution, even if the data was collected from a narrow, specific user group.