Question: What data sources do they use for creating their dataset?

Evidence:
raw_evidence: For the first round, we used a BERT-Large model BIBREF10 trained on a concatenation of SNLI BIBREF1 and MNLI BIBREF22, and selected the best-performing model we could train as the starting point for our dataset collection procedure. For Round 1 contexts, we randomly sampled short multi-sentence passages from Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set BIBREF23. Contexts are either ground-truth contexts from that dataset, or they are Wikipedia passages retrieved using TF-IDF BIBREF24 based on a HotpotQA question.
For the second round, we used a more powerful RoBERTa model BIBREF25 trained on SNLI, MNLI, an NLI-version of FEVER BIBREF26, and the training data from the previous round (A1). After a hyperparameter search, we selected the model with the best performance on the A1 development set. Then, using the hyperparameters selected from this search, we created a final set of models by training several models with different random seeds. During annotation, we constructed an ensemble by randomly picking a model from the model set as the adversary each turn. This helps us avoid annotators exploiting vulnerabilities in one single model. A new non-overlapping set of contexts was again constructed from Wikipedia via HotpotQA using the same method as Round 1.
For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. We trained an even stronger RoBERTa model by adding the training set from the second round (A2) to the training data.
raw_evidence: For the first round, we used a BERT-Large model BIBREF10 trained on a concatenation of SNLI BIBREF1 and MNLI BIBREF22, and selected the best-performing model we could train as the starting point for our dataset collection procedure. For Round 1 contexts, we randomly sampled short multi-sentence passages from Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set BIBREF23. Contexts are either ground-truth contexts from that dataset, or they are Wikipedia passages retrieved using TF-IDF BIBREF24 based on a HotpotQA question.
For the third round, we selected a more diverse set of contexts, in order to explore robustness under domain transfer. In addition to contexts from Wikipedia for Round 3, we also included contexts from the following domains: News (extracted from Common Crawl), fiction (extracted from BIBREF27, and BIBREF28), formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), and causal or procedural text, which describes sequences of events or actions, extracted from WikiHow. Finally, we also collected annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset BIBREF29. We trained an even stronger RoBERTa model by adding the training set from the second round (A2) to the training data.

Answers:
Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set, Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), RTE5
Wikipedia (of 250-600 characters) from the manually curated HotpotQA training set, formal spoken text (excerpted from court and presidential debate transcripts in the Manually Annotated Sub-Corpus (MASC) of the Open American National Corpus), causal or procedural text, which describes sequences of events or actions, extracted from WikiHow, annotations using the longer contexts present in the GLUE RTE training data, which came from the RTE5 dataset