{"instruction":"Here is the the abstract for a scientific paper:
  It has been shown that word embeddings derived from large corpora 
  tend to incorporate biases present in their training data. Various 
  methods for mitigating these biases have been proposed, but recent 
  work has demonstrated that these methods hide but fail to truly 
  remove the biases, which can still be observed in word 
  nearest-neighbor statistics. In this work we propose a probabilistic
  view of word embedding bias. We leverage this framework to present a 
  novel method for mitigating bias which relies on probabilistic 
  observations to yield a more robust bias mitigation algorithm. 
  We demonstrate that this method effectively reduces bias according 
  to three separate measures of bias while maintaining embedding quality 
  across various popular benchmark semantic tasks
What would be some questions that the paper could answer?",
"output":"How is embedding quality assessed?
  What are the three measures of bias which are reduced in experiments?
  What are the probabilistic observations which contribute to the more robust algorithm?"}