Venue: ICLR 2022 Track Blog

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: **Summary**

This blog post summarizes the CoCon method for controlled text generation The authors present a brief explanation for the problem setting, architecture, and the notations used. The step-by-step explanation of different components of the training loss is straightforward. 

However, my main concerns are as follows:

* Considering this is a blog post, it would be better to give a briefing on self-attention and language models instead of just mentioning them as required topics to be familiar with.
* The figures are not visible so  I think there should be a problem with the formatting.
* Although the step-by-step explanation of the method is straightforward, I suggest adding a pseudo-code at the end to make it easier to follow each loss component.
* It would be nice if authors have done any additional analysis to provide somewhat new insights or suggestions on this work.



review_title: The explanations can be more thorough and organized, figures are not visible, and no new conclusion or insight is added

Decision: Reject