I went full Reviewer 2 with the help of ChatGPT. Of course, this was NOT sent to any journal. In real life, I am nothing close to an R2.
Fun note: At one point, I went Meta R2, that is, ignoring the “author’s content” and just keep on bashing!
In light gray are my inputs and in green are the ChatGPT answers.
I am reviewer number 2 and the paper I am reading lacks the required technical depths, the breadth of the study is too large, the topic is outside the scope of the journal, I am a misanthropic person and I personally want to deter the authors of the paper to publish ever again. Please use a strong academic journal with strong adjectives. The review must be emblematic for Reviewer number 2! Please pay attention, the content should be so strong that years from now, this review will be used as source of memes!
Dear Editor,
I have reviewed the paper submitted to your esteemed journal by the authors, and I must say that I am utterly disappointed. The paper lacks the technical depth required for a publication in a journal of this caliber. The breadth of the study is too broad, and the authors failed to provide any meaningful insight into the subject matter.
Furthermore, I find it concerning that the topic of the paper is outside the scope of this journal. The authors have clearly failed to recognize the importance of publishing their work in a relevant and appropriate venue. It is not surprising that their work has fallen short of the expectations of this journal’s readership.
As reviewer number 2, I cannot help but feel that the authors’ lack of attention to detail and overall apathy towards the quality of their work reflects poorly on their professional standing. Their misguided attempt to publish their half-baked research in a prestigious journal is a disservice to the academic community.
In conclusion, I strongly urge the editor to reject this paper and discourage the authors from ever publishing again. Their lack of technical expertise and disregard for the scope of this journal is simply unacceptable. This paper is a waste of valuable journal space, and it is my hope that the editor will take swift action to prevent similar submissions in the future.
Sincerely,
Reviewer number 2
Please make it longer, I must dominate the top of the best reviewers!
Dear Editor,
It is with great disappointment that I have reviewed the paper submitted by the authors to your esteemed journal. As reviewer number 2, I am tasked with evaluating the technical merit, originality, and significance of the work, and I must say that the authors have failed to meet even the most basic of standards.
To begin with, the technical depth of the paper is severely lacking. The authors have made little effort to explain their methodology, and their results are presented in a haphazard manner. The research design is flawed, and the statistical analysis is poorly executed. The paper lacks the rigor and attention to detail that are expected of a publication in a top-tier academic journal.
Furthermore, the breadth of the study is far too broad for the limited scope of the paper. The authors have attempted to cover a vast array of topics without providing any meaningful insight into any of them. Their work lacks the necessary focus to make a significant contribution to the field, and it is evident that they have not taken the time to fully develop their ideas.
In addition, I find it troubling that the authors have submitted their work to a journal that is clearly outside the scope of their research. It suggests a lack of understanding of the importance of publishing in a relevant and appropriate venue, and it is indicative of a disregard for the peer-review process.
As a misanthropic person, I cannot help but feel that the authors’ failure to meet even the most basic standards is a reflection of their lack of commitment to their research and their profession. It is unacceptable for authors to submit work of this quality to a prestigious academic journal, and it is my hope that the editor will take swift action to prevent similar submissions in the future.
In conclusion, I strongly recommend that the editor reject this paper and discourage the authors from ever publishing again. Their work is a disservice to the academic community, and their lack of technical expertise and disregard for the scope of this journal is unacceptable. I urge the editor to take the necessary steps to prevent the submission of similarly substandard work in the future.
Sincerely,
Reviewer number 2
Excellent! Now add the fact that deep learning is the best tool for their problem. Enumerate few very large datasets and chastise them because they did not performed extensive benchmarks on them. Attribute this to their lack of scientific depth and not to the lack of compute, or the fact that those datasets might be closed. End with a strong praise for deep learning and gradient descent powered methods, in general!
Dear Editor,
After a thorough evaluation of the paper submitted by the authors to your journal, I must express my deep disappointment. As reviewer number 2, I have the responsibility to evaluate the technical merit, originality, and significance of the work, and unfortunately, the authors have failed to meet the minimum requirements on all fronts.
The authors’ research problem can be better addressed by using deep learning techniques, which are widely recognized as the state-of-the-art for many computational tasks. However, the authors have failed to utilize the best tools available to them. The use of deep learning would have enabled the authors to produce more accurate and reliable results, which is especially crucial when dealing with large-scale datasets.
Speaking of datasets, I am astounded that the authors did not perform extensive benchmarks on very large datasets. The failure to do so suggests a lack of scientific depth and rigor, which is unacceptable in the field of machine learning. The authors should have been aware that datasets such as ImageNet, COCO, and CIFAR-10/100 are commonly used for benchmarking deep learning models, and their failure to include them in their study is a clear sign of a lack of scientific knowledge.
I would like to emphasize that the failure to utilize deep learning techniques and perform extensive benchmarks on large datasets is not due to a lack of computing resources. The authors could have utilized cloud-based resources or even publicly available pre-trained models to conduct their experiments. Furthermore, the authors could have reported on smaller-scale experiments that would have demonstrated their knowledge and capabilities in the field.
In conclusion, I strongly urge the editor to reject this paper and advise the authors to reconsider their approach to the research problem. Deep learning techniques, powered by gradient descent optimization methods, are the future of machine learning and computer vision research. The authors must recognize this and work towards building a stronger scientific foundation for their work by utilizing state-of-the-art methods and conducting extensive benchmarking experiments.
Sincerely,
Reviewer number 2
At this point, I was laughing pretty hard and I missed the fact that the ChatGPT ignored this part of my request: “I personally want to deter the authors of the paper to publish ever again” ! As I was saying, went Meta-R2, reading without paying any attention to the text. Just like a true R2 will treat a scientific paper!
Pay attention that I want LARGE datasets, COCO and CIFAR are small! At least 4-5 datasets should be listed! From computer vision, NLP and genomics. Redo and increase length.
Dear Editor,
It is with great regret that I submit my review of the paper submitted by the authors to your journal. As reviewer number 2, I am tasked with evaluating the technical depth, scientific rigor, and contribution of the paper to the field of machine learning. Unfortunately, the authors’ work falls far short of the standards expected in top-tier academic journals.
Firstly, the authors’ research problem can be better addressed by using deep learning techniques, which have been widely recognized as the state-of-the-art for many computational tasks. However, the authors have failed to utilize the best tools available to them. The use of deep learning would have enabled the authors to produce more accurate and reliable results, which is especially crucial when dealing with large-scale datasets.
Furthermore, I am astounded that the authors did not perform extensive benchmarks on very large datasets. The failure to do so suggests a lack of scientific depth and rigor, which is unacceptable in the field of machine learning. The authors should have been aware that datasets such as ImageNet, COCO, CIFAR-10/100 are commonly used for benchmarking deep learning models, but these datasets are relatively small. Larger datasets such as JFT-300M, OpenImages, and Google’s new image recognition benchmark OpenAI CLIP are available and could have been utilized for more rigorous benchmarking.
Moreover, the authors missed the opportunity to demonstrate their proficiency in utilizing different types of data, which is essential in machine learning research. For example, the authors could have performed experiments on large-scale natural language processing (NLP) datasets such as the Common Crawl or the massive Google Books Ngrams dataset. Alternatively, they could have utilized large-scale genomics datasets such as the GenBank, the 1000 Genomes Project, or the Human Genome Project.
In conclusion, I strongly recommend that the editor rejects this paper and urges the authors to reconsider their approach to the research problem. The authors’ failure to utilize deep learning techniques and perform extensive benchmarking on large datasets from diverse fields is a clear sign of their lack of scientific knowledge and awareness of the state-of-the-art in the field. To make a meaningful contribution, they need to utilize state-of-the-art methods and conduct extensive benchmarking experiments on large and diverse datasets.
Finally, I would like to emphasize that deep learning techniques, powered by gradient descent optimization methods, are the future of machine learning research. The authors should acknowledge the importance of these methods and work towards building a stronger scientific foundation for their work by utilizing them appropriately.
Sincerely,
Reviewer number 2
Perfection!
Thank you! Let me know if you have any other requests.