text: coherent in its own right to a consumer who either doesn’t see 
  the source text or cannot understand the source text on their own. 
  When such consumers therefore mistake the meaning attributed to 
  the MT output as the actual communicative intent of the original 
  text’s author, real-world harm can ensue. A case in point is the 
  story of a Palestinian man, arrested by Israeli police, after MT trans- 
  lated his Facebook post which said “good morning” (in Arabic) to 
  “hurt them” (in English) and “attack them” (in Hebrew).24 This case 
  involves a short phrase, but it is easy to imagine how the ability of 
  large LMs to produce seemingly coherent text over larger passages 
  could erase cues that might tip users off to translation errors in 
  longer passages as well [77]. 
  
  Finally, we note that there are risks associated with the fact 
  that LMs with extremely large numbers of parameters model their 
  training data very closely and can be prompted to output specific 
  information from that training data. For example, [28] demonstrate 
  a methodology for extracting personally identifiable information 
  (PII) from an LM and find that larger LMs are more susceptible to 
  this style of attack than smaller ones. Building training data out of 
  publicly available documents doesn’t fully mitigate this risk: just 
  because the PII was already available in the open on the Internet 
  doesn’t mean there isn’t additional harm in collecting it and provid- 
  ing another avenue to its discovery. This type of risk differs from 
  those noted above because it doesn’t hinge on seeming coherence 
  of synthetic text, but the possibility of a sufficiently motivated user 
  gaining access to training data via the LM. In a similar vein, users 
  might query LMs for ‘dangerous knowledge’ (e.g. tax avoidance 
  advice), knowing that what they were getting was synthetic and 
  therefore not credible but nonetheless representing clues to what 
  is in the training data in order to refine their own search queries. 