text: 4.3 Encoding Bias 
  It is well established by now that large LMs exhibit various kinds of 
  bias, including stereotypical associations [11, 12, 69, 119, 156, 157], 
  or negative sentiment towards specific groups [61]. Furthermore, 
  we see the effects of intersectionality [34], where BERT, ELMo, GPT 
  and GPT-2 encode more bias against identities marginalized along 
  more than one dimension than would be expected based on just the 
  combination of the bias along each of the axes [54, 132]. Many of 
  these works conclude that these issues are a reflection of training 
  data characteristics. For instance, Hutchinson et al. find that BERT 
  associates phrases referencing persons with disabilities with more 
  negative sentiment words, and that gun violence, homelessness, 
  and drug addiction are overrepresented in texts discussing mental 
  illness [61]. Similarly, Gehman et al. show that models like GPT-3 
  trained with at least 570GB of data derived mostly from Common 
  Crawl16 can generate sentences with high toxicity scores even when 
  prompted with non-toxic sentences [53]. Their investigation of GPT- 
  2’s training data17 also finds 272K documents from unreliable news 
  sites and 63K from banned subreddits. 