A research team is pre-training a large language model. They observe that the model's loss on the pre-training objective is still decreasing, indicating better performance on that specific task. However, when they periodically evaluate the model on a diverse suite of benchmark tasks it has not been trained on, its performance on those tasks has started to decline. What does this scenario most strongly suggest about the training process in relation to its primary goal?

Evaluating Pre-training Strategies for Generalizability

In the context of pre-training a large language model, the primary and ultimate measure of success is achieving the lowest possible value for the loss function on the pre-training task.