text: 8 CONCLUSION 
  The past few years, ever since processing capacity caught up with 
  neural models, have been heady times in the world of NLP. Neural 
  approaches in general, and large, Transformer LMs in particular, 
  have rapidly overtaken the leaderboards on a wide variety of bench- 
  marks and once again the adage “there’s no data like more data” 
  seems to be true. It may seem like progress in the field, in fact, de- 
  pends on the creation of ever larger language models (and research 
  into how to deploy them to various ends). 
  
  In this paper, we have invited readers to take a step back and 
  ask: Are ever larger LMs inevitable or necessary? What costs are 
  associated with this research direction and what should we consider 
  before pursuing it? Do the field of NLP or the public that it serves 
  in fact need larger LMs? If so, how can we pursue this research 
  direction while mitigating its associated risks? If not, what do we 
  need instead? 
  
  We have identified a wide variety of costs and risks associated 
  with the rush for ever larger LMs, including: environmental costs 
  (borne typically by those not benefiting from the resulting technol- 
  ogy); financial costs, which in turn erect barriers to entry, limiting 
  who can contribute to this research area and which languages can 
  benefit from the most advanced techniques; opportunity cost, as re- 
  searchers pour effort away from directions requiring less resources; 
  and the risk of substantial harms, including stereotyping, denigra- 
  tion, increases in extremist ideology, and wrongful arrest, should 
  humans encounter seemingly coherent LM output and take it for 
  the words of some person or organization who has accountability 
  for what is said. 