Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper explores model extraction attacks on BERT models pre-trained on Arabics such as AraBERT and SudaBERT. The paper also proposes a method for protecting model extraction attacks through adding the noise in form of pre-training the victim model on more epochs.
strengths_and_weaknesses: **Strengths:**

- The paper is written well, clearly and it is concisely. It presents the problem in an understandable way. Good abstract and literature review.
- The conducted experiments are presented well. Nice use of figures and tables.
- The paper provides a good amount of details of used datasets.
- The paper follows ICLR paper requirements.

**Weaknesses**

- There are not enough details of the architecture used during pre-training(example: size of BERT model, number of encoders, dimension of the model).
suggested_changes: - It’s nice that you provided some hyperparameters in the experimental setup/appendix. The paper would also benefit from including the details about the architecture used and other hyperparameters that are not reported(like the type of optimizer used).
- Minor changes: correct CONCULSATION(section name).
review_title: null

Meta-review: Please reformat your abstract so it shows correctly in OpenReview.
Decision: Invite to present