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              [html]<thead><tr><td>Question : What is the name of the Russian mercenary group?</td></tr></thead><tbody><tr><td>Answer : Wagner group.</td></tr><tr><td>Qpestion : Where is the Wagner group?</td></tr><tr><td>Answer : In Syria.</td></tr></tbody>
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  she primarily had n-gram models in mind, the conclusions remain 
  apt and relevant. 
  
  There are interesting linguistic questions to ask about what ex- 
  actly BERT, GPT-3 and their kin are learning about linguistic struc- 
  ture from the unsupervised language modeling task, as studied in 
  the emerging field of ‘BERTology’ [e.g. 110, 133]. However, from 
  the perspective of work on language technology, it is far from clear 
  that all of the effort being put into using large LMs to ‘beat’ tasks 
  designed to test natural language understanding, and all of the 
  effort to create new such tasks, once the existing ones have been 
  bulldozed by the LMs, brings us any closer to long-term goals of 
  general language understanding systems. If a large LM, endowed 
  with hundreds of billions of parameters and trained on a very large 
  dataset, can manipulate linguistic form well enough to cheat its 
  way through tests meant to require language understanding, have 
  we learned anything of value about how to build machine language 
  understanding or have we been led down the garden path? 