text: 6.3 Summary 
  In this section, we have discussed how the human tendency to 
  attribute meaning to text, in combination with large LMs’ ability 
  to learn patterns of forms that humans associate with various bi- 
  ases and other harmful attitudes, leads to risks of real-world harm, 
  should LM-generated text be disseminated. We have also reviewed 
  risks connected to using LMs as components in classification sys- 
  tems and the risks of LMs memorizing training data. We note that 
  the risks associated with synthetic but seemingly coherent text are 
  deeply connected to the fact that such synthetic text can enter into 
  conversations without any person or entity being accountable for it. 
  This accountability both involves responsibility for truthfulness and 
  is important in situating meaning. As Maggie Nelson [92] writes: 
  “Words change depending on who speaks them; there is no cure.” 
  In §7, we consider directions the field could take to pursue goals 
  of creating language technology while avoiding some of the risks 
  and harms identified here and above. 
  
  7 PATHS FORWARD 
  In order to mitigate the risks that come with the creation of in- 
  creasingly large LMs, we urge researchers to shift to a mindset of 
  careful planning, along many dimensions, before starting to build 
  either datasets or systems trained on datasets. We should consider 
  
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