text: We note that the change from n-gram LMs to word vectors dis- 
  tilled from neural LMs to pretrained Transformer LMs is paralleled 
  by an expansion and change in the types of tasks they are use- 
  ful for: n-gram LMs were initially typically deployed in selecting 
  among the outputs of e.g. acoustical or translation models; the 
  LSTM-derived word vectors were quickly picked up as more effec- 
  tive representations of words (in place of bag of words features) 
  in a variety of NLP tasks involving labeling and classification; and 
  the pretrained Transformer models can be retrained on very small 
  datasets (few-shot, one-shot or even zero-shot learning) to perform 
  apparently meaning-manipulating tasks such as summarization, 
  question answering and the like. Nonetheless, all of these systems 
  share the property of being LMs in the sense we give above, that 
  is, systems trained to predict sequences of words (or characters or 
  sentences). Where they differ is in the size of the training datasets 
  they leverage and the spheres of influence they can possibly affect. 
  By scaling up in these two ways, modern very large LMs incur new 
  kinds of risk, which we turn to in the following sections. 