a 1 point Succinctly explain why the pretrained vanilla model was able to achieve an accuracy ofabove 10 whereas the non-pretrained model was notb 2 points Take a look at some of the correct predictio

(a) The pretrained model was able to achieve an accuracy of above 10% because it was trained on a large corpus of text data before being fine-tuned on the specific task of predicting birth places. This allowed the model to learn general language patterns and relationships that are useful for many tasks, including predicting birth places.

(b) One reason why the model behavior of being unable to tell whether it retrieved or made up a birth place may cause concern for user-facing systems is that it could lead to incorrect information being presented to users. For example, if the model predicts a birth place that is actually made up, but the user assumes it is accurate, they may make decisions based on that incorrect information. Another reason is that it could erode trust in the system, as users may become skeptical of the accuracy of the information being presented to them. For example, if the model consistently predicts birth places that cannot be verified, users may stop relying on the system altogether.

(c) A strategy the model might take for predicting a birth place for a person's name that it has not seen before is to use contextual information from the text surrounding the person's name. For example, if the text mentions that the person is a famous musician, the model might predict a birth place that is commonly associated with musicians, such as Nashville, Tennessee. This could cause concern for the use of such applications because the predicted birth place may not be accurate, and users may assume that it is based on the model's high accuracy on other predictions. Additionally, if the model consistently predicts birth places based on contextual information rather than actual knowledge, it may erode trust in the system and lead to users seeking out alternative sources of information