text: alone, we have seen the emergence of BERT and its variants [39, 
  70, 74, 113, 146], GPT-2 [106], T-NLG [112], GPT-3 [25], and most 
  recently Switch-C [43], with institutions seemingly competing to 
  produce ever larger LMs. While investigating properties of LMs and 
  how they change with size holds scientific interest, and large LMs 
  have shown improvements on various tasks (§2), we ask whether 
  enough thought has been put into the potential risks associated 
  with developing them and strategies to mitigate these risks. 
  
  We first consider environmental risks. Echoing a line of recent 
  work outlining the environmental and financial costs of deep learn- 
  ing systems [129], we encourage the research community to priori- 
  tize these impacts. One way this can be done is by reporting costs 
  and evaluating works based on the amount of resources they con- 
  sume [57]. As we outline in §3, increasing the environmental and 
  financial costs of these models doubly punishes marginalized com- 
  munities that are least likely to benefit from the progress achieved 
  by large LMs and most likely to be harmed by negative environ- 
  mental consequences of its resource consumption. At the scale we 
  are discussing (outlined in §2), the first consideration should be the 
  environmental cost. 