You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Since our approach allows us to generate queries on every passage of the target corpus, one question is that whether retrieval system trained this way simply memorizes the target corpus or it also generalize on unseen passages. Furthermore, from an efficiency standpoint, how many synthetic training examples are required to achieve maximum performance.To answer these questions, we uniformly sample a subset of documents and then generate synthetic queries only on that subset.Results on BIOASQ 7 are shown in Figure 4, where x-axis denotes the percentage of sampled documents.We can see that retrieval accuracy improves as passage coverage increases.The peak is achieved when using a 20\% subset, which covers 21\% of the reference passages.This is not surprising because the number of frequently discussed entities/topics are typically limited, and a subset of the passages covers most of them.This result also indicates that the learned system does generalize, otherwise optimal performance would be seen with 100\% of the data.

Question: How will the generator perform when the target corpus have different query/document (answer) distributions from the training datasets? For example, what if users in target application often ask much longer questions (e.g., longer than 12 tokens), to express more complex query intents?
when the target corpus have different query/document (answer) distributions from the training datasets, the generator will show performance based on learned system to show optimal performance.