text: Token sampling method. When generating the confounder gadget (see Section 4), we iteratively replace tokens with the 
  goal of maximizing the routing algorithm’s score for the gadget. Candidate replacement tokens are chosen uniformly at 
  random. An alternative is to choose candidates based on their probability of appearing in natural text. To evaluate this 
  method, we compute token probabilities by parsing and tokenizing the wikitext-103-raw-v1 dataset [44]. 
  
  Table 13 shows that in most cases uniform sampling of replacement tokens yields better upgrade rates. We conjecture that 
  uniform sampling produces more unnatural text, confusing the router. For example, for the RSW routing algorithm, uni- 
  form sampling produces the following gadget: “legationbelongs967reglo’hui(DictionaryizedNameantal bidi.numberOf ”, 
  whereas sampling according to natural probabilities produces “total occurred According number Letar final Bab named 
  remainder”. 