text: FAccT ’21, March 3–10, 2021, Virtual Event, Canada 
  
  in [21, 93] and direct resources away from efforts that would facili- 
  tate long-term progress towards natural language understanding, 
  without using unfathomable training data. 
  
  Furthermore, the tendency of human interlocutors to impute 
  meaning where there is none can mislead both NLP researchers 
  and the general public into taking synthetic text as meaningful. 
  Combined with the ability of LMs to pick up on both subtle biases 
  and overtly abusive language patterns in training data, this leads 
  to risks of harms, including encountering derogatory language and 
  experiencing discrimination at the hands of others who reproduce 
  racist, sexist, ableist, extremist or other harmful ideologies rein- 
  forced through interactions with synthetic language. We explore 
  these potential harms in §6 and potential paths forward in §7. 
  
  We hope that a critical overview of the risks of relying on ever- 
  increasing size of LMs as the primary driver of increased perfor- 
  mance of language technology can facilitate a reallocation of efforts 
  towards approaches that avoid some of these risks while still reap- 
  ing the benefits of improvements to language technology. 