text: Finally, we would like to consider use cases of large LMs that 
  have specifically served marginalized populations. If, as we advo- 
  cate, the field backs off from the path of ever larger LMs, are we 
  thus sacrificing benefits that would accrue to these populations? 
  As a case in point, consider automatic speech recognition, which 
  has seen some improvements thanks to advances in LMs, including 
  both in size and in architecture [e.g. 8, 59, 121], though the largest 
  LMs typically are too large and too slow for the near real-time needs 
  of ASR systems [60]. Improved ASR has many beneficial applica- 
  tions, including automatic captioning which has the potential to 
  be beneficial for Deaf and hard of hearing people, providing access 
  to otherwise inaccessible audio content.26 We see two beneficial 
  paths forward here: The first is a broader search for means of im- 
  proving ASR systems, as indeed is underway, since the contexts of 
  application of the technology aren’t conducive to using ever larger 
  LMs [60]. But even if larger LMs could be used, just because we’ve 
  seen that large LMs can help doesn’t mean that this is the only 
  effective path to stronger ASR technology. (And we note that if we 
  want to build strong ASR technology across most of the world’s 
  languages, we can’t rely on having terabytes of data in all cases.) 
  The second, should we determine that large LMs are critical (when 
  available), is to recognize this as an instance of a dual use problem 
  and consider how to mitigate the harms of LMs used as stochastic 
  parrots while still preserving them for use in ASR systems. Could 
  LMs be built in such a way that synthetic text generated with them 