You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Tables 2 and 3 shows the average accuracy of six non-linguistic tasks (palindrome classification, isogram classification, tautonym classification, odd even classification, decimal operation and median) fine-tuned using different BERT and DeBERTA representations respectively. We note that the models pretrained on all three domains outperformed the non-pretrained model (NP). This suggests that the results of experiments in Section 5 generalize to new text corpora for pretraining, and do not rely on having access to text on specific topics during pretraining. This is a non-trivial result, since it suggests for example, that the higher performance of pretrained models on tasks such as palindrome and anagram classification is not due to the pretrained models having seen information about such concepts during pretraining. This is especially so since the results even generalize to ROC stories, which contain no information on such technical concepts. 

Question: According to the paper, does BERT is overfitted?
They say that the reason of good performance of fine-tuned model is not caused by task specific knowledge.