text: Strubell et al. also examine the cost of these models vs. their 
  accuracy gains. For the task of machine translation where large 
  LMs have resulted in performance gains, they estimate that an 
  increase in 0.1 BLEU score using neural architecture search for 
  English to German translation results in an increase of $150,000 
  compute cost in addition to the carbon emissions. To encourage 
  more equitable access to NLP research and reduce carbon footprint, 
  the authors give recommendations to report training time and 
  sensitivity to hyperparameters when the released model is meant 
  to be re-trained for downstream use. They also urge governments to 
  invest in compute clouds to provide equitable access to researchers. 
  Initiatives such as the SustainNLP workshop5 have since taken 
  up the goal of prioritizing computationally efficient hardware and 
  algorithms. Schwartz et al. [115] also call for the development of 
  green AI, similar to other environmentally friendly scientific de- 
  velopments such as green chemistry or sustainable computing. As 
  shown in [5], the amount of compute used to train the largest deep 
  learning models (for NLP and other applications) has increased 
  300,000x in 6 years, increasing at a far higher pace than Moore’s 
  Law. To promote green AI, Schwartz et al. argue for promoting 
  efficiency as an evaluation metric and show that most sampled 
  papers from ACL 2018, NeurIPS 2018, and CVPR 2019 claim accu- 
  racy improvements alone as primary contributions to the field, and 
  none focused on measures of efficiency as primary contributions. 
  Since then, works such as [57, 75] have released online tools to 
  help researchers benchmark their energy usage. Among their rec- 
  ommendations are to run experiments in carbon friendly regions, 
  consistently report energy and carbon metrics, and consider energy- 
  performance trade-offs before deploying energy hungry models. 
  In addition to these calls for documentation and technical fixes, 
  Bietti and Vatanparast underscore the need for social and political 
  engagement in shaping a future where data driven systems have 
  minimal negative impact on the environment [16]. 