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.

[Q]: Our method is purely text-based, and ignores the publication date and the source of the article. It combines task-specific embeddings, produced by a two-level attention-based deep neural network model, with manually crafted features (stylometric, lexical, grammatical, and semantic), into a kernel-based SVM classifier. 
 Question: what types of features were used?
[A]: stylometric, lexical, grammatical, and semantic


[Q]: The two main data sets that we will use to evaluate our architecture are the 2014 i2b2 de-identification challenge data set BIBREF2 and the nursing notes corpus BIBREF3 . 
 Question: Which two datasets is the system tested on?
[A]: 2014 i2b2 de-identification challenge data set BIBREF2 nursing notes corpus BIBREF3


[Q]: Suppose we are given a specific language model such as GPT-2 BIBREF6, GROVER BIBREF8, or CTRL BIBREF7, and it is characterized in terms of estimates of either cross-entropy $H(P,Q)$ or perplexity $\mathrm {PPL}(P,Q)$.

We can see directly that the Neyman-Pearson error of detection in the case of i.i.d. tokens is:

and similar results hold for ergodic observations.

Since we think of $H(P)$ as a constant, we observe that the error exponent for the decision problem is precisely an affine shift of the cross-entropy. Outputs from models that are better in the sense of cross-entropy or perplexity are harder to distinguish from authentic text.

Thus we see that intuitive measures of generative text quality match a formal operational measure of indistinguishability that comes from the hypothesis testing limit. 
 Question: Which language models generate text that can be easier to classify as genuine or generated?
[A]:
Outputs from models that are better in the sense of cross-entropy or perplexity are harder to distinguish from authentic text.