Not necessarily. The quality of the answers generated by a language model like GPT-3 is influenced by many factors, such as the complexity and diversity of the training data, the architecture of the model, and the task being performed.
The number of tokens, or the amount of text used to train the model, is also an important factor, but it's not the only one.
In general, increasing the number of tokens used to train a language model can improve its performance on certain tasks, such as generating coherent and fluent text or answering questions. However, there is a point of diminishing returns where adding more tokens may not lead to significant improvements in performance, and may even lead to overfitting or other issues.
Therefore, it's important to balance the number of tokens used to train a language model with other factors, such as computational resources, training time, and the specific requirements of the task at hand.