Imagine a team of engineers is training a large language model. They observe that after a long period of rapid improvement achieved by adding more and more training data, the model's error rate on a fixed test set has stopped decreasing and has flattened out. Even doubling the training dataset size again results in a negligible improvement. Analyze the fundamental factors that could be contributing to this performance plateau, explaining why simply adding more data is no longer effective.