instruction:
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.
question:
jiant System Overview ::: jiant Components
Tasks: Tasks have references to task data, methods for processing data, references to classifier heads, and methods for calculating performance metrics, and making predictions.
Question: Does jiant involve datasets for the 50 NLU tasks?
answer:
Yes

question:
We decided to use sentences involving at least one race- or gender-associated word. The sentences were intended to be short and grammatically simple. We also wanted some sentences to include expressions of sentiment and emotion, since the goal is to test sentiment and emotion systems.
Question: What criteria are used to select the 8,640 English sentences?
answer:
Sentences involving at least one race- or gender-associated word, sentence have to be short and grammatically simple, sentence have to include expressions of sentiment and emotion.

question:
Therefore, in order to enhance the prediction model's robustness, we will adopt differential privacy (DP) method. DP is a system for sharing information about a dataset publicly by describing groups' patterns within the dataset while withholding information about individuals in the dataset. DP can be achieved if the we are willing to add random noise to the result. For example, rather than simply reporting the sum, we can inject noise from a Laplace or gaussian distribution, producing a result that's not quite exact, that masks the contents of any given row. It intuitively requires that the mechanism that outputs information about an underlying dataset is robust to one sample's any change, thus protecting privacy. A mechanism ${f}$ is a random function that takes a dataset $\mathcal {N}$ as input, and outputs a random variable ${f}(\mathcal {N})$. For example, suppose $\mathcal {N}$ is a news articles dataset, then the function that outputs compound score of articles in $\mathcal {N}$ plus noise from the standard normal distribution is a mechanism [7].
Question: How does the differential privacy mechanism work?
answer:
A mechanism ${f}$ is a random function that takes a dataset $\mathcal {N}$ as input, and outputs a random variable ${f}(\mathcal {N})$.