text: 5 DOWN THE GARDEN PATH 
  In §4 above, we discussed the ways in which different types of 
  biases can be encoded in the corpora used to train large LMs. In 
  §6 below we explore some of the risks and harms that can follow 
  from deploying technology that has learned those biases. In the 
  present section, however, we focus on a different kind of risk: that 
  of misdirected research effort, specifically around the application 
  of LMs to tasks intended to test for natural language understanding 
  (NLU). As the very large Transformer LMs posted striking gains 
  in the state of the art on various benchmarks intended to model 
  meaning-sensitive tasks, and as initiatives like [142] made the mod- 
  els broadly accessible to researchers seeking to apply them, large 
  quantities of research effort turned towards measuring how well 
  BERT and its kin do on both existing and new benchmarks.19 This 
  allocation of research effort brings with it an opportunity cost, on 
  the one hand in terms of time not spent applying meaning captur- 
  ing approaches to meaning sensitive tasks, and on the other hand in 
  terms of time not spent exploring more effective ways of building 
  technology with datasets of a size that can be carefully curated and 
  available for a broader set of languages [65, 91]. 