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: The median age is 17 for depressed class versus 19 for control class suggesting either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age for connecting to their peers (social homophily.) Our findings are consistent with the medical literature BIBREF10 as according to BIBREF52 more women than men were given a diagnosis of depression. 
 Question: What insights into the relationship between demographics and mental health are provided?

Output: either likely depressed-user population is younger, or depressed youngsters are more likely to disclose their age more women than men were given a diagnosis of depression


Input: Consider Input: Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks.
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42). To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models. 
 Question: How do they control for annotation artificats?

Output:  we use several of the MCQA baseline models first introduced in BIBREF0


Input: Consider Input: String kernels represent a way of using information at the character level by measuring the similarity of strings through character n-grams. 
 Question: What is a string kernel?


Output: String kernel is a technique that uses character n-grams to measure the similarity of strings