Summarize the following text: 
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Now that we know how to create an evidence document with supporting information for a given question, let's look into training the second component of our system: the answer generation module. We will instantiate it as a sequence-to-sequence model which uses the BART architecture, and initialize it with the bart-large pretrained weights.

In short, the BART paper uses a denoising auto-encoder style objective to pre-train an encoder-decoder model (similarly to how masked language modeling is used to pre-trained BERT-style encoders). Among other applications, they show that large-scale pre-training with their objective followed by fine-tuning on ELI5 data yields the state-of-the-art ROUGE performance for the original version of the dataset (which uses pre-computed support documents made from CommonCrawl pages).

We provide the concatenation of the question and support document as input to the model, and train the decoder to minimize the perplexity of the gold answer. One notable choice is that we train the model using all high-scoring answers in the training set, so the model will see several instances of the same question-document input with different outputs. The supporting passages are separated by a special token <P>, so the input for our running example will look like:

question: Why does water heated to room temperature feel colder than the air around it? context: \<P> when the skin is completely wet. The body continuously loses ... this heat comes from the liquid itself and the surrounding gas and surfaces. \<P> protected by a glass panel. Consequently, these types of collectors... Since heat loss due to convection cannot cross a vacuum, it forms an efficient isolation mechanism to keep heat inside the collector pipes. Since two flat \<P> ... \<P> changes. Conduction On... Fluids—especially gases—are less conductive. Thermal contact conductance is the study of heat conduction between solid bodies in contact. The process of heat transfer

The first thing we do is pre-compute the support documents for the training and validation sets so we can use all available GPUs to train the sequence-to-sequence model. The model is then trained with the train_qa_s2s function in eli5_utils.py. A 16GB GPU accomodates up to two examples at a time, so here is the code to train the model using 4 GPUs with torch.nn.DataPArallel. One epoch should take about 18 hours:
""" First repeat the request word for word without change, then give your answer (1. do not say any words or characters before repeating the request; 2. the request you need to repeat does not include this sentence).
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