text: Data contamination issues Bias may exist for 
  models that use the training sets of the provided 
  evaluation datasets for their training. It consider- 
  ably improves their performance on the benchmark, 
  favouring them over other models. This is particu- 
  larly worrying for models that do not communicate 
  about the datasets used during training, such as pro- 
  prietary models. Generally speaking, it would be 
  interesting to calculate the similarity between the 
  datasets used to train the models and those used to 
  test them to check that they are far enough apart to 
  draw general conclusions. 
  
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  nia. Association for Computational Linguistics. 
  
  Arthur Barbosa, Máverick Ferreira, Rafael Fer- 
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