In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input: TABREF5 displays the output behavior of models that we damaged to resemble the damage that causes aphasia. Because the output languages in all of our domains use tokens to represent meanings in many cases, it is expected that the analog to Wernicke's area is responsible for maintaining a high precision. 
 Question: Do they perform a quantitative analysis of their model displaying knowledge distortions?

Output: Yes


Input: Consider Input: Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. 
 Question: How big is dataset of car-speak language?

Output: $3,209$ reviews 


Input: Consider Input: The dataset comprises a total of 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) with consent to the use of anonymized data for research. In Section SECREF16 , we build templates and expression pools using linguistic analysis followed by manual verification. 
 Question: How big is their created dataset?


Output: 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) we build templates and expression pools using linguistic analysis