text:
Is Utilizing AI Tools for Conducting Literature Reviews in Academic Research Advisable?
Recently, I've come across various AI tools that claim to assist in conducting literature reviews by quickly summarizing research papers, identifying key themes, and even suggesting relevant literature.
While these AI tools appear to be promising in terms of efficiency and comprehensiveness, I'm concerned about their reliability and the potential impact on the quality of the literature review. My questions are:

Is it advisable to use AI tools for literature reviews in academic research?
How do these tools compare to traditional methods in terms of accuracy and depth of analysis?

IMHO, reviews published in journals should only be written by people who are experts on the topic (who probably don't need AI).  Otherwise the review is likely to be unreliable (at best) and likely to mislead students and early career practitioners.  My view of these AI tools in general is that they are fine for automating the drudgery of things you already know how to do well, but you can't rely on the accuracy of what they produce, so you need to be expert enough to safely validate it yourself.  If it is for a student report or dissertation, this is less of a consideration.

If you are able to successfully use AI to do any meaningful literature review, please let us know how. I have tried this (for theoretical computer science / formal methods), with no success.

By "AI", do you specifically mean language models, or AI in general? Because auto-summarization and semantic analysis are things, and the answer will be (somewhat) different if that's what you mean.

I meant AI in general. @Ray

@ImanMohammadi Then Ben's answer is right. Autosummarization and keyword extraction could probably be used to filter a large number of papers into a shortlist of papers that are more likely to be relevant. (Whether any particular tool is better at that than existing search engines is another question, but there's no technical reason why they couldn't be.)

You can use any tool you want to identify papers, but you have to read them yourself
It really depends on what you propose to use an AI tool for.  To write a literature review you want to (a) identify relevant papers; and (b) read those papers and summarise them in an appropriate way.  An AI tool might potentially be useful in identifying relevant papers, but it is not a substitute for reading those papers yourself.  Here it is notable that AI tools have a history of "hallucination" whereby they may incorrectly summarise a resource or even invent a resource that does not actually exist.

So basically, use AI tools as search engines, not as copywriters.

@vsz - and at that point one should just start with a good research librarian and an appropriate literature search engine, not ChatGPT and the like.

I currently treat AI tools like that they way we were always warned to use Wikipedia back in the early days - You can't trust it to not be completely invented, even thought it may look entirely plausible, so use it as a precompiled starting point but verify everything before you rely on it.  As @Jon Custer said, at that point it really only makes sense to use if it's your best available starting resource. That may indeed be the case, but caveat emptor

@NickJ : This is a good practice for Wikipedia even today, especially around topics which generate lots of political and ideological interest.

@vsz That's a good practise. Period. If you want to rely on something, veryify it. No need to specify that that's exclusive to Wikipedia and LLM-generated content, it's just as true for a research articles (who wrote it and what are their interesests? Does it align with other articles?). Obviousy it depends on 'the thing' and where it came from how much effort should go in verifying it (for example Wikipedia is statistically more trustworthy than most sources, but it still deserves verification, and LLM-generated content is significantly less trustworthy).

The problem with this approach (using AI tools as search engines/to identify papers) is that it sometimes invents papers that don't exist.

To be brief, if used appropriately, AI tools are fantastic and can be used to improve the speed of development and the quality of literature reviews. But if used inappropriately, AI tools can speedily lead you into plagiarism and academic fraud with all their disastrous consequences.
I teach how to (and how not to) use AI tools for academic research and publications, including specifically for literature reviews. I will summarize here some key points from a presentation I gave on this topic:

Slides
YouTube recording

You can refer to the slides and recording for details.
First of all, it is crucial to generally understand when it is appropriate or not to use generative AI (not just for academic research, but in general). Here's a framework I have adapted from James Chapman to summarize some key considerations:

Edit: the flowchart has been reconfigured based on comments by @BryanKrause.
For literature reviews, the key considerations from this framework are:

Do you need absolutely true results? Yes, we do. That's the point of a literature review. We want to know what the literature actually says.
Can you verify the accuracy of the results yourself? This is the key issue.

Let me first talk about the risks
Generative AI tools like ChatGPT will confidently answer questions that we ask about what the literature says. But we must be very clear and not fool ourselves: generative AI is well known to make up answers out of thin air. (The technical, polite term for this is "hallucination".) So, if we submit and publish literature analysis by tools like ChatGPT without verifying it, we have an extremely high chance of committing academic fraud.
Another serious issue is that ChatGPT and similar tools copy answers from various sources, often with minimal paraphrasing, without citing its sources. A thorough study on the topic found that "59.7% of GPT-3.5 Outputs Contained Some Form of Plagiarized Content".
However, it is crucial to not make the mistake of thinking that AI tells lies or that it plagiarizes. In fact, AI cannot tell lies or plagiarize. Only humans who have an ethical sense of what is true or false and a consciousness of when we are telling the truth, exaggerating, being dishonest, sneaky, cheating, etc. can tell "lies" or "plagiarize". AI has no ethical sense whatsoever. It is just following instructions and random fluctuations to give answers to our questions with no inherent sense of whether the answer is true or false. That's how it works. So, if we submit answers from generative AI that are plagiarized or have false information, we, the academic authors who chose to use generative AI, are 100% responsible. We can't say, "ChatGPT made me do it."
So, if we want true answers, we need an "oracle", that is, an authoritative source that we can depend on to verify if the answers are true or not. That oracle must be US HUMANS, the literature review authors. No AI can do this for us. But--here's the good news--AI can most certainly help us.
How to use generative AI appropriately
Because of these well-known risks of generative AI, many tools are rapidly being developed that help us to use generative AI appropriately. I can't keep up with all of them, but two of the best ones that I recommend are Consensus and ScholarAI. The key idea is the same:

When we ask them questions about the literature, they only give answers for which they can find support in the literature.
They cite and link the literature to support their answers so that we can verify their answers ourselves.

The second point is crucial. If I didn't sufficiently scare you with my first part above, let me repeat: we should never trust unverified answers produced by generative AI. We must verify everything it tells us ourselves and take full responsibility for it. These excellent tools make it very easy to do that. They identify literature much faster and in more detail than we can easily do ourselves. As long as we verify everything these tools give us, we can find them to be extremely helpful research assistants.
Again, my linked presentation and recording give much more details on further risks, how to avoid them, and how to effectively use these tools, with a brief case study of an article I wrote with extensive assistance of generative AI.

That flowchart is poor because the concerns expressed on the right hand side are also valid on the left, but are skipped entirely if you take that route. It would be far better to express the same information without the flow chart and make clear those are all concerns.

@BryanKrause, in logical theory yes, but in practice, I think the flow chart actually works quite well. I am hard-pressed to find cases where we need absolutely true responses that involve sensitive data. Absolutely true responses are typically public information, which is not sensitive data. Moreover, absolutely true results are typically common knowledge or in the public domain (non-copyrightable), so I don't find that legal ownership is much of an issue. Could you give a concrete example where the flowchart fails to classify a significant case? I'm asking you for my own learning.

Using GenAI for data cleaning (just one example).

@BryanKrause You're right; I'll need to rework my flowchart. Thanks. If you think of any other examples, I'd appreciate if you could add them to these comments.

I'm having trouble thinking of any cases where data are used and the results don't need to be accurate. Sorting resumes/applications, grading papers, any research project. Organizing or prioritizing your email (in particular as a professor in the US: FERPA).

@BryanKrause A huge use of generative AI is generating ideas such as strategies, inspiration, suggestions for how to structure articles, etc. There is no strictly true or false answer for these.

Like any data gatherer bot, the AI can't be guilty of anything.  The people that own the AI certainly can be.

@ScottSeidman We agree that AI cannot be responsible for or guilty of anything. However, I'm not sure what you mean by the people who "own" the AI. In the context of this question, my focus is on the people who choose to USE the AI, which is us researchers in this case. If by "own" you mean the big corporations who develop and market the AI, that is a slightly different issue, but I certainly agree with you that they bear considerable responsibility for what AI capabilities they choose to make public.

I'd add another: elicit.com has a pretty nice feature where you can upload a set of papers you're studying and ask simple questions about them. "how many cases? how many controls? Does the paper mention adjusting for XYZ?" and get the answers in a neat table. Those sorts of direct questions about a provided block of text tend to be much much better suited to LLM's than general questions.

There are different types of literature reviews. One crucial difference is whether the cited literature needs to be complete e.g. because you want to run a meta-analysis or whether you (as expert in the field) report stragtegies and best (and possibly bad) practices and e.g. want to provide the reader with a few selected papers that you judge to be good showcases. In the latter case, using AI to generate a candidate paper list from which you then choose those to cite is IMHO fine.

But currently I wouldn't rely on AI answers for meta-studies (though, OTOH, if the usual search is conducted and then AI digs up some relevant papers in addition, I wouldn't know why they should not be included if they are legitimate and satisfy quality criteria and listed in the category "AI xyz found in addition")  but this is hypothetical and the more important question then would maybe be why the normal search missed them...

@BryanKrause Thanks for your comments. I have revised the flowchart  accordingly.

I feel like this has a risk of being more work than a non-AI "assisted" review. As mentioned in other answers, AI tools are prone to "hallucinating" incorrect information. In fact, the first time I'd seen someone use ChatGPT for "hey, can you find me a paper on XYZ", it returned a plausible-looking paper, complete with BibTeX-formatted citation... that didn't exist (for more examples of hallucinated references see e.g. this Retraction Watch article). So for any tool that works along similar lines (or is just a wrapper around ChatGPT) if you ask it to point you at papers, your first step now will (well, should) be looking up whether all the papers even exist. That seems to me like it'd add an extra step compared to looking for related literature on Pubmed or the like "by hand", where at least you'll know what you get exists to begin with.

Just as I do make intense use of Wikipedia for suggestions and pointers to things that I might not been aware-of, I'd suggest using various AI things similarly. For that matter, other peoples' opinions! :) Follow up on things yourself, and don't rely on what amounts to "hear-say". :)

As of now, I advise strongly against using Generative AI in most forms of research because of the risk of hallucinations. But the technology develops quickly and reasonable people may hold other opinions.
For background, I am not an academic in the traditional sense. I am a practicing lawyer (licensed only in Nevada) that works in technology law and used to be a computer programmer. I also publish occasionally in law review journals and similar academic outlets.
I have tested multiple versions of generative AI to see if they could be used in my practice. The answer I have come to each time is that generally they are far more trouble than they are worth at this point in time.
As several other answers have mentioned, current iterations of generative AI have a tendency to "hallucinate". This is a complex topic, but essentially this means that they can frequently give answers that appear correct while being wildly incorrect. I have previously posted a video about why I believe this makes current AI more dangerous than useful. That video is now slightly dated even though it was only published a few months ago and the available generative AI has improved since then, but my fundamental opinion that currently available models are more trouble than they are worth remains the same as I continue to observe and test various versions. This however is likely to change as the technology continues to improve.
Opinions can reasonably differ. I know some lawyers that think these AIs are useful sounding boards. That is a reasonable opinion, but that must be approached with great caution. It is my opinion that with the present, fairly high, risk of hallucination that verifying what the AI provided can be harder than simply doing the research from scratch. But if you choose to use generative AI for anything where getting the facts correct matters, then everything the AI outputs should be very carefully vetted and checked.
Also, I am responding to the call of the question which focused on reliability and impact on the quality of the research. There are varying opinions regarding the ethics of the way present AIs were created as well as how they can be used that I won't address here beyond acknowledging their existence. Also, nothing I say on this forum should ever be viewed as legal advice, but works created by generative AI are most likely not able to be protected by copyright. This may be a substantial problem for anyone hoping to have generative AI create a large portion of a paper for publication.

Short version: This is an experimental technology and you should use it like you would an experimental gun - use a light load, wear goggles, check it after every shot, and don't rely on it without a backup.
Long version: ChatGPT 4 Turbo (paid API version) uses RAG - Retrieval Augmented Generation - which significantly cuts down on hallucinations, if prompted correctly. But it only currently has a memory limit of 70,000 tokens, or approximately 50,000 words. It's much less for the web version, where amnesia begins between 5,000 and 15,000 tokens. When it processes papers, or multiple papers, or various sources that total more than that, it begins to hallucinate even if set up with firm instructions to only use real data.
There is a different LLM well-suited for processing long or multiple papers, Claude, which has a window of up to 200,000 tokens.
If you rely on AI to give you a summary it can't, either due to not having appropriate quasi-cognitive function (e.g., ChatGPT can't process math papers), or to insufficient window size, it will make up whatever it can.
If you do understand the limitations of the exact model you're using, then it's fine to use them to significant extent, as long as you mention that. Given how complex the field is, you'd probably have to be an AI researcher - this answer is 0.1% of what you'd need to know.
If you don't understand what the model is doing, how it works, how large the context window is, and what is currently in-context and what out of it - then don't trust GAI. Use it as a search tool, but always check the paper you're summarizing yourself.
Even if you see an exciting 85% accuracy rate, the 15% that GAI is creating unconstrained by RAG can turn out to be the perfect answer to your research question: exact, complete, well-explained, and completely wrong.

I don't advise use of AI tools for anything like you've mentioned.

Can you back this up or is this just an opinion?

One could assert that the built-in spelling and grammar checks in Word are "AI" in their suggestions. But one still takes them with a grain of salt.

@JonCuster You could, but I wouldn't find it a very interesting discussion.

@Mayou36 I meant to return to this answer, but didn't. Now, people have provided many more detailed answers and I would just be adding similar statements. I've upvoted those I agree with.

GenAI does not understand, does not reason. It's basically a parrot on steroids, repeating and creating from learned content based on probabilities.
Thus you cannot rely on it to follow arguments, scientific discourse. And therefore it seems unsafe or unreliable to use it in literature review.
Say, for example, there is a flaw in the line of reasoning of a paper you are citing, a wrong assumption or fake data that is easily identifiable. As a human reader you should be able to spot this and your review would contain a mention of your concerns (or discard the work entirely as part of the review), an LLM might just blindly summarize the work even though it's bogus.

This is a value judgment you would have to make, there is no gold standard answer.
However before you do make a decision, take into account what AI is:

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with
intelligent beings. The term is frequently applied to the project of
developing systems endowed with the intellectual processes
characteristic of humans, such as the ability to reason, discover
meaning, generalize, or learn from past experience.

In other words, it is designed to learn from your interactions with it, the more you utilize it the more it learns to be better than you at the task you are utilizing it for. Basically, you're teaching it to learn how to be better than you. So, the question of who is utilizing who ought to be answered beforehand, especially because those AI tools didn't appear ex nihilo, some entity—in most cases —with a pecuniary motive designed them.
Take this case for an example:

You've probably heard the story: A young buck comes into a new job full of confidence, and the weathered older worker has to show them
the ropes—only to find out they’ll be unemployed once the new employee
is up to speed. This has been happening among humans for a long
time—but it may soon start happening between humans and artificial
intelligence.
Put all of this together and there’s the potential that companies could use data they’ve harvested from workers—by monitoring them and
having them interact with AI that can learn from them—to develop new
AI programs that could actually replace them. If your boss can figure
out exactly how you do your job, and an AI program is learning from
the data you’re producing, then eventually your boss might be able to
just have the program do the job instead.

In closing, while AI tools do offer advertised benefits one must be cognizant of the unadvertised costs too.

This aswer is not completely accurate. The majority of AI systems are not continuously learning by interacting with users. This would require a massive capacity of computing power. Even models like chat gpt can be used in safe privacy protected manner. My university wrapped it up with their own interface and all data are encrypted.

@TheDoctor If it really is OpenAI's ChatGPT, you've been mislead. OpenAI promises not to use data given to their API for training any more, but they used to, and regular ChatGPT does use information for this purpose.

That said, @Ptah-hotep: while “it is designed to learn from your interactions with it” is true (in a sense, depending on what we mean by “it”, “designed” and “learn”) of many such systems, that's not anything inherent or intrinsic to it being AI. It's a property of the particular computer system.

ChatGPT is not the only LLM available. I run LLAMA2 and many other models on my on instances. There is absolutely no learning involved in these modeld unless I want to fine tune it. Nonetheless, my institution host their own ChatGPT and they promise that no data is sent to OpenAI, everything is done locally.

@wizzwizz4 Even when they were using user data, they were not using it for continuously learning. What they meant by using user data is that they would collect your interactions, and may or may not choose to use it as part of the dataset for the next generation of models. Its a significant difference. Its generational not continuous.

Most current AI systems have problems with "continuous learning" or "life long learning" -- due to something called "catastrophic forgetting". Essentially, if the training of a system is performed over an extended period of time (where the training data used early on is later not accessible), the system starts struggling with the stuff it could do well early on. So, while your data may be used to train a later version of a system (e.g. GPT4.5) or something, most (all?) current LLMs don't learn from interactions as they happen.


text:
Stuck in a pattern of studying hard for exam but not getting the desired grade
I could be studying 2.5 months for an exam, every single day, doing all the exercises that I can. Doing all the old exams that I can get my hand on. Never falling behind on submissions for the course. Yet, when it is time for the exam, I write like I am a total moron. Sloppy mistakes everywhere. Doesn’t matter how slow I go through the questions. I will still misread something or read too much into a question or interpret it completely differently from what is asked for.
I know I have exam fright and I am trying to get through it by writing the exam rather than bail on myself the day of it, or hand in blank because I know I won’t get an A.
Is there anyone here in the same position who may offer advice? I would greatly appreciate it!
I know I will be advised to not care about grades, but it is just one of those things that I cannot not care about.

What do you do in the last 24 hours before the exam?

"doing all the exercises that I can." Do you give your solutions of the exercises to your prof/TA to grade? Do you get the marks and feedbacks? Most importantly, do you read the feedbacks and figure out if there are things wrong and why they are wrong?

@Buffy most of my exams start at 8 and end 12:30. So when it comes to my physical/mental well being I make sure I get rested. Have breakfast/snacks for the day after and a water bottle and a coffee. I revise. Revisit A-level questions from old exams. Make sure I know where I am placed. Times I’ve even sat in the classroom where I will write before the exam to “familiarize myself with the environment”

@Nobody Not all times. We can compare our solutions to a solution manual compiled and written by one of our professors. I seem to not have a problem with most questions. If I get stuck on a question then I do ask the TA

What field is this? Or is it more general?

I'm going to suggest that you talk to a professional counsellor about this. Freezing is an extreme reaction to a fear of failure (perhaps, I'm not really qualified to diagnose).
Your actions in studying seem to be pretty good; long term study, rest, diet...
Butterflies in the stomach are natural before a big event, whether a high dive into the pool or an exam. But freezing is a form of self defeating behavior that is probably more psychological than anything. A counsellor can help.
Even Simone Biles had a period of failure in and important meet because of (I think) losing mental focus at the wrong moment. It can happen to anyone.
Many (most) universities will provide an office of student support that can probably help.

It's not a solution, but a google search for "comfortable in your own skin" might turn up some ideas.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

sort: votes


text:
Your best advice for exam anxiety
This may be a question that does not belong here, but...
What advice would you give someone who is preparing for exams and cares about grades. What advice do should I follow during the exam and after the exam as someone who stresses about grades?
Please be harsh, I really do not believe that "grades are just grades in the end".

So what exactly are the alternatives to "grades are just grades in the end"? I mean, one alternative view is that grades are a measure of your own personal worth, or that they measure whether or not you are a "failure", in which case the best advice is to work on abandoning that belief.

This question will probably strongly benefit from some details to narrow the question down, in particular: 1) Do you have any idea what your anxiety stems from? 2) How does your anxiety manifest? 3) What kind of exam are we talking about? 4) Do you have any chance to obtain accommodation? (Please [edit] your question to clarify.)

Related: https://academia.stackexchange.com/questions/82591/how-to-help-reduce-students-anxiety-in-an-oral-exam

Duplicate: https://academia.stackexchange.com/questions/184943/how-to-reduce-stress-before-your-qualifying-exam

Related: https://academia.stackexchange.com/questions/158523/what-can-i-do-when-anxiety-causes-me-to-perform-poorly-in-oral-exams-and-class-d

What always helped me in exam-like cases (it was really the same if you took an exam, give a presentation or have a job interview for me), is just saying to myself "you studied all you could, there is nothing you could change anymore so just take the things as they come". For me this somehow always worked well :)

The way to avoid anxiety, assuming it isn't a psychological condition, is to build confidence over the weeks (weeks!) leading up to the exam by using effective learning techniques.

Take effective notes and then summarize them in writing. Don't assume that if you have heard something once you should remember it. Active learning.

Work lots of exercises as appropriate. Get feedback on your solutions.

Ask questions as needed and record the results.

Don't cram the night before the exam. Get proper sleep. My most memorable F grade was after cramming much of the night before an open book test. Ugh.

If it is a psychological condition, your university probably has a counseling office that can provide advice.

You are not the only student who suffers from anxiety around exams. It is so common, that in every university I have been, student counselors offered something (could be a meeting or individual sessions, or...) to help students with that anxiety. So best advise I can give is to make use of what is on offer at your university. They are the professionals, we are just random persons on the internet.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
If I find an error in my archived doctoral thesis, should I immediately tell my advisor or wait to see if it will be published before doing so?
I recently found a mistake in my thesis while submitting a postdoc application. I realized that I messed up my reaction rate quantification of one protein where I assigned too large of an intensity value for an unreacted protein. After fixing the quantification I saw that my conclusion I wrote in the text was unaffected, but a few points on a graph did change. Although I don't think this is a small typo, my corrected work will not upend any conclusions I make in my thesis. My examiners had nothing to say about this graph. While putting together my thesis, I think I might have forgotten to double check this graph.
So on the one hand, I do want to tell my boss to see if there's something I can do to correct this and mitigate any potential effect on my career. However I'm not sure how he's going to react and if this might cause drama in a department he has already left (went to another uni). I think he was also quite happy to have me finally out of the lab. So if I were to write an errata, it would be just for presenting a corrected rate graph.
On the other hand, some of my friends with PhDs suggest that it may be better to correct the error when/if that figure is needed for publication or patent. Their rationale is that more people read a peer-reviewed publication article and that's where it really counts. They've said that only a handful of people read thesis compared to papers and I should've stopped reading my thesis long after passing my defense. One of my friends even told me that professors may make future students redo certain experiments before publication, so my data may not even be used at all.
Currently my thesis is under embargo for two years. Regardless, do you think telling him now vs. later makes a big difference? What's holding me back from telling him now is that I'm starting off in a new job and I don't know what it would take to make a correction. Furthermore, when I was his student I didn't think he was the easiest guy to get along with.

Could you clarify how this question is distinct from your previous Found mistake in analyzing experimental data after submitting PhD thesis; what should I do?

@Anyon I included more details and one thing I want to explore here is about how soon I need to tell my supervisor given all the feedback I've gotten from my friends since I asked that question.


text:
How should I cite a paper on arxiv correctly?
I'm writing my thesis. For example, this paper on arxiv.
https://arxiv.org/abs/1409.0473
I'm using Overleaf. I used Export Bibtex Citation on arxiv and paste the information into the .bib file. The result is shown as in the graph.
But my prof says I should add the detailed information of the references such as pages, number, and volume.
I'm very confused, how should I do this? Are this information contained in the Bibtex Citation from arxiv?

The exported information is
@misc{https://doi.org/10.48550/arxiv.1409.0473,
  doi = {10.48550/ARXIV.1409.0473},

  url = {https://arxiv.org/abs/1409.0473},

  author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},

  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {Neural Machine Translation by Jointly Learning to Align and Translate},

  publisher = {arXiv},

  year = {2014},

  copyright = {arXiv.org perpetual, non-exclusive license}
}

Not an answer, but the paper in your example has actually been published at a conference (see the comment field on arXiv), so unless you need to cite something specifically that's only present in the arXiv version, you should mention the conference as the venue.

@GoodDeeds Thank you, I have updated question. But how should I modify my .bib file and cite it in my text to show the information? Is the information of the conference contained in the above exported bib information?

@user900476 you will probably either have to add the info manually or look at the conference/journal websites where the papers have been "officially" published if they provide exportable citations.

@Sursula Thank you! I see. I've found the more detailed bibtex citation source on https://dblp.org/rec/journals/corr/BahdanauCB14.html. So never use arxiv bibtex at first I guess?

@user900476 I believe ICLR (and for that matter arXiv) doesn't have the concept of page numbers and volumes. All you can include is venue name and year (and identifier if you cite from arXiv).

@user900476 There is nothing wrong with using arXiv bibtex, as long as you want to cite the arXiv version of a paper, and not the conference/journal version (if it exists).

@GoodDeeds Thank you! My prof might be a little bit stubborn and might not know the arxiv citation form. (He's not reseaching on AI)

@GoodDeeds btw the arxiv citation form still does not show the paper is from arxiv? i.e. when I use \cite{https://doi.org/10.48550/arxiv.1409.0473}, the result is shown as [1] in the graph. It does not show it is from arxiv? i.e. the link. Although the url information is contained in the exported bibtex citation form?

@user900476 That is probably because of the bibliography style you are using (probably \bibliographystyle in your latex file). You can find more information in the documentation or on [tex.se] Stack Exchange.

@GoodDeeds I'm using \bibliographystyle{plain}

Talk to your professor politely and patiently, and explain that arXiv does not have volume and page numbers. If they still insist, then remove the arXiv reference from our bibliography list and use a footnote reference instead.

TLDR: You should include detailed information about the paper you cite, and you have not done this. This is why your prof is confused.

If the screenshot is how the citation looks in your thesis, then it's obvious why your advisor is confused.
You have shown only the authors' names, title, and year. Anybody reading your thesis will be confused how they are expected to search for the paper, and how they can be sure that they found the same paper that you meant.
This is why, when citing a journal article, you include not only authors' names, title and year, but also journal title, volume, number, and preferrably pages. This will give readers a very clear indication which work has been cited.
If you cite a paper from arXiv, you should include information that will explicitly say that this is an arXiv preprint, and give readers some obvious way to find the paper on arXiv. An arXiv url perfectly fits this purpose, or alternatively you can use any wording that will convey the same information, e.g. "digital preprint, arXiv #1409.0473", or something similar. Your prof should be able to tell which style is preferrable for your thesis.
Note that arXiv url is already present in your bibtex record, so it is just a matter of TeX or BibTeX configuration to have it shown in the citation. My TeXfu is rusty, but I remember tinkering a lot with configs to show the citations exactly as I see fit in my thesis some 10 years ago.
And regarding the comments by your prof, most probably they thought that this is a citation from a journal, and therefore asked to include the said information. Without anything indicating that this is an arXiv publication, it is quite natural to think that this is a journal citation. If you tell them that this is an arXiv paper, they most probably would not require journal name etc., but would require an arXiv url or similar information as I have explained above.

Maybe your boss is not aware that your article is available in a standard way / in a journal?
From the link you provided I exported the citation the way GoogleScholar suggests it, I think in this way it becomes clear that this is an arXiv article.
@article{bahdanau2014neural,
   title={Neural machine translation by jointly learning to align and translate},
   author={Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
   journal={arXiv preprint arXiv:1409.0473},
   year={2014}
}

Keep in mind: What information is being displayed in your references of the compiled PDF in what style/format depends on your bibtex settings. I think in your file bibtex items of class "misc" are simply not displayed with all the information you want to see.


text:
Should PhD students tell their supervisor about the paper they review?
I am a PhD student and have recently accepted a review request from a (quite prestigious) journal. I wonder whether I should tell this to my supervisor. Of course, the benefit of telling them is that my supervisor might help me improve my review; the drawback is that it might affect the anonymity of the review.
Should I do that? I guess the answer depends on the field (I'm in computer science) and many other factors.
Edit: I actually meant whether I should tell my supervisor about the exact paper I review. (The paper is online and both of us know it.) If I do so, my supervisor might comment on my review and help me improve it.
Edit 2: Thank you all for your answers! I finally decided to ask the journal editor about this issue.

"The paper is online"? Before it is even reviewed? Do you mean there is a preprint available about the same thing?

@Karl In many fields, it's completely normal to post online the same version of a paper that you submit to a journal, before it is reviewed.

I'm not sure a PhD student should be reviewing a paper without the involvement of the supervisor.  A PhD is like an apprenticeship and learning to review papers is part of that training.  Your supervisor should be at least checking your work at this stage, just as a master craftsman would be checking the work of their apprentices before it went out of the door.  I've never yet had an editor object to me including a PhD student in a review for this purpose - how else should we expect to get good peer reviewers if it isn't part of the training?

@DikranMarsupial: I beg to disagree about PhD students not reviewing without their supervisor. There may be an important cultural difference behind this, though (see my answer). Over here, however, I'm concerned about two patterns I've seen far too often. a) Our PhD "students" are almost always fully qualified professionals (even though without experience). But academic culture/structure over here is quite prone to power abuse, and PhD positions are particularly at risk. Belittling the abilities of PhD students together with micromanagement is one way this abuse of power takes...

... The very same MSc holder outside academia could easily be trusted to work in a position where they write expert opinions that actually have legal or health consequences (I'm thinking e.g. of food chemists in public health/consumer protection/quality assurance). b) I've also seen far too many situations where I have the very strong suspicion that a supervisor told their student to do a review and then submitted under their own name, without informing the editor. I'm thus very happy to see that you first consult the editor about doing a review together with your student, ...

... and I trust that you a good teacher who helps and lets their students grow rather than keeping them under your power. But to me, the possibility of a) or b) hiding behind honest motives like yours is the larger risk compared to a PhD student submitting a badly written review. Also, if we decide that students need better training how to judge papers and write reviews, this could be done without compromising the actual review process. (Just as we train them e.g. in labwork practica to handle instrumentation without compromising actual research experiments.)

"(even though without experience)" that is part of the reason the supervisor should be involved.  It isn't a matter of a lack of ability, there is just a limit to the number of papers you can read and absorb during a PhD.  I'm sorry to hear that there is power abuse over this sort of thing.  PhD students are absolutely your peers in potential, and they should be treated as such (i.e. the way you would want to have been treated as a PhD student).  The bad PhD supervisors you mention should be weeded out and not allowed to supervise any more.

I used to teach a short course on the review process for my department's PhD students, and it is a necessary component of their training, but they still are not ready to review papers by themselves, mostly because they only tend to have a rather narrow view of the field (through no fault of their own, just experience), and and they still benefit from collaborating with their supervisors on the review (hopefully less and less input from the supervisor each time).

I should add, PhD students are definitely part of the audience for most research papers, so having students involved is a definite benefit for the author.  If the paper is not written in a way that is accessible for PhD students, it probably needs to be rewritten in order to effectively communicate to the widest audience.

It is unethical to give him any details about the paper or authors whose paper you are reviewing. I was once reviewing a paper, and one of the authors of that paper was a potential Ph.D. examiner. When I hinted this to my supervisor, he advised that it is unethical to disclose any information related to reviewed paper.

There is a difference between telling your supervisor about your request and involving them actively. Telling them that you received such a request does not necessarily impact the anonymity of the review, as you don't have to tell them specifics, just that you received it. They will probably be pleased that you are taking part in the peer review process, as this is something that is kind of expected in academia.
Learning how to do (good) peer reviews is a process that your supervisor may be able to help you with, and it can thus be beneficial to talk with them about it. Again, if you want to maintain the anonymity, you don't have to talk specifics, but you can still get general advice from them.
When I received my first peer review request, I told my supervisor. He was able to help me with some doubts I had (like reasons why you should recommend rejecting a paper or instead request major revisions) without ever having a single glimpse at the paper in question.

You and I have similar opinions. I think your answer is better than mine.

@EthanBolker Thanks, but your answer isn't bad either :-)

Thanks for the answer! Actually I'm thinking about telling my supervisor the paper I'm reviewing (which is freely online) and asking him to comment on my review. I don't want him to influence my decision, though. Anyway, I'm a bit worried that the anonymity of the review might be affected.

There is a chance that a student gets a review request after their advisor rejected it and recommended asking the student instead. In which case the advisor has probably already seen the paper and thus can offer some more detailed guidance. In any case, letting your advisor know you are doing a review is a good idea indeed.

"When I received my first peer review request, I told my supervisor." Same here. I was a bit unsure because it was a single-reviewer journal and it felt like a lot of responsibility, so I wanted a 2nd opinion before accepting the review task.  He never said he suggested me, but I had my suspicions

@user53923: however, that chance does not allow the reviewer to violate confidentiality.

@AnonymousPhysicist: I agree that the answer would benefit from emphasizing more the important difference between telling that you do a review, and asking general (i.e. abstracted from the case at hand) advise vs. violating confidentialiy by disclosing information about the actual manuscript (without approval by the editor). However, IMHO this answer is not unambiguously unethical.

@cbeleites It does not, but my point is they would not have to, there is nothing the student has seen that the supervisor has not in this case (or vice versa).

The answer does indeed depend on many factors.
Part of your advisor's job is to teach/show you how the profession works. If you think their help or insight could substantially improve your review, then asking them for that help may be appropriate.
You could ask  if they think that help would compromise the review process.
You could consider asking the editor if showing your review to your advisor would be OK.
You should not be asking your advisor for help deciding on your recommendation to the editor - just on matters of style, like a good way to suggest changes.

"You should not be asking your advisor for help deciding on your recommendation to the editor" -- why not? I'd generally disagree.

@usul Perhaps. If you wanted to do that I might suggest checking with the editor first.

"You could consider asking the editor if showing your review to your advisor would be OK."  This is mandatory, not optional.

Yes!
Like with every other non-trivial activity which you are doing for the first time, it's best to have an instructor with you. Your advisor is able to teach you how to write a good reviewer report, and might also point out things you missed or should emphasize. Furthermore, your advisor is likely to be interested that you are doing peer review.
Involving your advisor doesn't compromise the anonymity of the review. It just means you tell the editor that the review was from both of you. You know each other are reviewers, but the authors still won't know. Potentially more concerning is if the article itself was supposed to be confidential, but that would be unusual. Many (most?) papers in computer science are posted on the arXiv before they are ever submitted to a journal.
So: no drawbacks, but there are potential benefits, so you should do it.

"if the article itself was supposed to be confidential, but that would be unusual."  It's not unusual in my field.  Check with the editor/editorial policy before acting.

Yes, I think you should discuss it with your advisor.
The upsides are significant and have been mentioned in other answers. The downsides are small:

At this point in your career, it is assumed that your advisor is aware of and guiding essentially all your professional work. In other words, I believe you are not violating norms to include your advisor here and it should be expected that this would happen.
At this point, any of your conflicts of interest are probably your advisor's conflicts also. Conversely, if your advisor has a conflict on the paper, you probably should not be reviewing it anyway as they are in a position of authority over you and at this point you are not quite autonomous. So the conflict-of-interest downsides are negligible.
I don't think there's any ethical problem with the advisor influencing your opinion and recommendation. Conversely, your responsibility is to gather information to make the most accurate recommendation possible, and consulting an expert is a great help.
Discussions between a PhD student and advisor are necessary for guiding you and can be kept confidential. It's like attorney-client privilege in my book.
Anonymity has known, accepted limits. There are a number of editors who know the reviewers, possibly someone who recommended you and at least suspects you're reviewing (this might be your advisor themself), etc. People often assign sub-reviewers or sometimes reach out for help in reviews to other experts. All of these people are trusted not to share this information any further.

(The situation could be different if you were late-career and used to reviewing big papers on your own, but in that case you wouldn't have asked this question.)

P.S.
Actually, what you should probably do is tell your advisor that you have been asked to review a paper without telling them what it is. Then ask if they think it's okay to help you review it. Let the advisor decide.

Sorry to (-1), but I see a huge ethical problem with this recommendation. In my field, reviewers agree to keep the manuscript content confidential (regardless of availability on pre-print servers). We do not know whether this is different for OP. The advise in this answer would be perfectly OK (even though I'd disagree with some expectations) if and only if the journal's reviewer guidelines allow PhD students to disclose manuscript information to their supervisor (IMHO highly unusual) or OP first asks the editor who will then typically be OK with the involvement of the supervisor.

@cbeleitesunhappywithSX That's fine, norms differ across fields and your comment is very useful. I also agree that checking with the editor first is a good idea. I don't follow the part about keeping the content confidential - the student wouldn't even need to share the submission, they could just ask the advisor's opinion on the preprint the advisor has already read. I do see an argument for keeping the identity of the reviewer confidential, or the fact that the paper was submitted to a particular journal.

pre-print available: I agree it would be fine to discuss the scientific merits of the pre-print (say, "Is their control group actually appropriate?") - but then in my field I'd be careful to not disclose the connection that I'm actually reviewing this. I read "supervisor might comment on my review" in the question as supervisor seeing the draft of the review, and that would violate the confidentiality requirements for reviewers in my field.

If the reviewers have sent you the review request on the condition that you will keep it anonymous, then you must keep it anonymous. They have asked your personal opinion, not a group opinion. If they want your advisor's review, they could have asked him directly.
Later on at some convenient time (when the paper has been decided), you can talk to your advisor about it.

I don't agree fully with this. Probably they would want the advisor's review, but they assume the advisor doesn't have time. Or they may have already asked the advisor and been rejected. Also, the anonymity barrier between an advisor and student doesn't make a lot of sense as I will discuss in an answer.

If the reviewers... => that should be editor, right?

If you are maintaining the confidentiality of the content of a submitted paper, that does not end "when the paper has been decided". It could conceivably end once the paper has been published, but if for example it is rejected that does not suddenly open it up as fair game because a decision has been rendered. The intellectual content is to be kept secret to not make the peer review process an avenue for disclosure of ideas and possible scooping of a result.

I received my PhD in Molecular Biophysics ten years ago from a graduate school in Texas. Since then I have been working in industry. As long as there is no conflict of interest, such as, if the manuscript author is your supervisor’s friend or competitor, then I don’t see an issue of letting your supervisor know. To do a good peer review, you actually have to spend time looking up references and do some research, all these efforts take away valuable time for your own thesis research. So it’s reasonable to inform your supervisor what you are doing with your time. However, if you wish your supervisor to help improve your review, then I am not onboard with your reviewing it or telling your supervisor. After all, the editor reached out to you, not your supervisor. If you don’t feel qualified, then in all honesty, you should not accept editor’s request.

You can always say informally that you are reviewing a paper from [Researcher Name] about [Topic], but you are usually not allowed to discuss openly the contents of the paper (either with coworkers or on some forum over the Internet) nor use the contents for your own research before it is actually published. If the paper is already on an online repository such as arXiv, that does not matter anymore as the paper is already public. Finally, there is no reason you could not ask for some help to evaluate a certain part of the paper you do not really understand. Reviewing is not only about evaluating papers, but also learning how to present complex ideas in a clear way and learning new stuffs in the process. It usually gives a lot of new ideas.
However, in some fields, such as in Biology, it is well known that a review request accepted by a professor will be actually written by some PhD student or postdoc, possibly several of them. This is common practice.
Finally, some journals also make both reviews and reviewers' names public at the end.

"you are usually not allowed to discuss openly the contents of the paper" Something the other answers missed.

"it is well known that a review request accepted by a professor will be actually written by some PhD student or postdoc, possibly several of them. This is common practice." - though this is indeed "well known" in biology, I have always heard of it as a rather negatively perceived activity... Lazy PIs, reviews that are so amateurish or pedantic there is no way they could have been written by the assigned reviewer. I've never heard someone admit that this is a thing they themselves do.

@BryanKrause Yeah, I also do not like it because someone will get the credit for the work of some other people, and this is not fair. The fair way would be to decline to review and provide the name of the person who will do the review, so that this person will get the well deserved credit for the work.

@KBS I think the value of that "deserved credit" is quite miniscule, in that participating in peer review is more of a professional service where it's not particularly necessary that other people know that you did the work, it's more important that YOU know you did the work, but yes, it is perfectly reasonable for a PI to write an editor and say "I do not have time to review this manuscript, but my (post doc/student) X would be qualified to do so" and typically the editor will not have a problem with this.

@BryanKrause Yes, there is credit to receive when reviewing as you participate to the community and this should be properly acknowledged. This is the reason why publons.com was introduced. If you are doing a good job as a reviewer, it should be recognized in the same way as you do a good job as a researcher, teacher, mentor, etc.

There is no difference between the rights and duties of a PhD student doing their first review or a professor doing their 1000th.

You're free to tell the fact that you got a paper to review, and which journal that request comes from.

You are bound by the reviewer guidelines of the journal in question, like any other reviewer.
Unless they explicitly state that fresh PhD students may do the
review with the help of their professor (which I'd consider extremely
unusual), you cannot disclose confidential information, e.g. content of the paper, to your supervisor.
In my field, we have such confidentiality requirements regardless of whether a preprint of the manuscript is publicly available or not.  If  a preprint is available, you can discuss the merits of the preprint as it is publicly known with your supervisor, but then cannot not disclose the fact you are reviewer for that manuscript.

You may ask your supervisor (or other colleagues) general questions, though.
Say, you find a dynamite plot "summarizing" 5 data points each into a bar and standard error - and wonder whether requesting the authors to change it into a point diagram is OK or asking too much: by putting the question in such abstract terms you do not reveal the content of the manuscript.

If you feel you are not fully qualified to do the review without asking colleagues (e.g. your supervisor) for advise about the content, that is typically possible by contacting the journal editor who will then give you leave to talk e.g. with your supervisor.

When I received my first review request, I told the fact at lunch break, and that I did yet feel qualified to judge a manuscript. My professor told me to please remember that I'm a fully qualified chemist since I graduated with my Diplom (nowadays Master), with all professional rights and duties that come along with it. And that papers are supposed to written so that they are understandable and the argumentation sufficiently clear convincing to any professional of roughly the (sub)field in question. In other words, to people like me.
IIRC, there was also an argument that already my Diplom thesis required me to critically judge the papers I studied wrt. what conclusions can be drawn from them.
Nowadays, I'd say: if a supervisor thinks their students need direct training on how to write a review, a format like a journal club is a very good opportunity to do so: the papers can be openly discussed to their merits in a group, and also what can reasonably be requested and to formulate this can be developed with a whole group of students. And this is possible without compromising a review process.
BTW, I suspect that I got this review request either because he as editor of the journal (of which I was not aware at the time - IIRC I did not see who the editor handling the manuscript was) suggested me or because he suggested me as alternative reviewer when he turned down the review request.

downvoter(s): care to explain what you disagree with?

The only reason I would be worried about anonymity is if I thought the paper deserved a bad review and I knew that my advisor was close to one of the authors. If I were in that situation I would question myself since my advisor is supposed to be my guide to the field, and if I can't trust their judgment then what am I doing? I mean, maybe it is a bad paper but hopefully your advisor would recognize that too.
If you think your advisor would be able to give the paper a fair review then there is no reason not to share it with them.

You've misunderstood the purpose of anonymity.  It's to prevent the impression that the reviewer has been bribed.

@AnonymousPhysicist I think bribery is a very small part of it... though there are two rather separate issues here: confidentiality of the reviewer's identity, and confidentiality of the contents of the reviewed paper. I do think this answer misses touching on the latter.


text:
TVöD rank for an engineer in Germany
I am a French citizen currently employed by an Institute part of the Helmholtz association. I had been looking forward to starting this position for a while and was a bit in a rush when signing my contract (not ideal I know). I overlooked an important point that I fear is too late to change now anyway.
The job posting did only mention that an "engineering degree" was required, without specifying if a master's degree was needed. I personally hold 2 master's degree from the double-degree program I followed during my studies (might be irrelevant). I have ~2.5 years of experience prior to this position.
Without going into details, it's quite a demanding job and de facto all people that I know of that had this position held at least a master's degree in the past. But I am classified as TVöD 11 level 1 nonetheless. Is this normal ? It is clearly stated in my contract that I would be payed as TVöD 11 but I was wondering if there were any law superseding contracts that forced public employers to pay you according to the degree you hold, or if it does not matter if clearly stated in the contract. I'm not too confident anyway but I would like to clarify it with my employer at least for those who'll follow.
Thanks for your help!
Edit: I talked about it with my boss. The justification for the pay group 11 is that my job does not encompass design activities. This is true, it is mostly a technician job even if they only employ engineers with a master's degree to do it. I think it's somewhat fair, even if the job is quite specialized. They would like to get it to a higher pay group but were blocked when they tried to in the past. They actually don't get to see my contract and were quite surprised that I was placed at level 1. They will discuss with the person responsible for this to get me to level 2. Will update when I get a definitive answer.

As far as I know, there must be a Tätigkeitsbeschreibung, i.e. a description of your tasks. You might not have seen it, but your employer probably had to write it. The salary is a result of this description.

no legal advice or expert here, but, if there is such a law superseding the contract you signed, you may have to check if your degree(s) are officially recognized in Germany. I remember something like "you have to have a degree from a German uni, to officially use the Ing. prefix" (I may be completely wrong, I am sure german people can correct me during their Mittagspause)

On level 11, a Master's degree cannot be required for this position. As already mentioned, you need to see the "Tätigkeitsbeschreibung" to check if pay scale category 11 is correct. Pay scale categories 9-12 are for those with a Bachelor's degree (or a degree from a University of Applied Sciences). Also check if in your ~2.5 years of experience, you did anything also mentioned in your "Tätigkeitsbeschreibung" and can prove that. The "Stufe 1" may simply be mentioned because it your job title was one that would count as a lower pay scale in TV-L, and then it would normally not count.

Note that having a Master's degree does not automatically mean you get a into a higher pay scale class. What is relevant is if the degree is needed for this position. For instance, technicians with a Master's degree are not paid pay scale level 13.

I have not heard about the Tätigkeitsbeschreibung before, I will check with my employer. This is a fixed-term position and as I said all the former employees had a master's degree or above. I wouldn't say it's absolutely necessary though so I don't think I can argue about that. But the field I work in is extremely specific and the whole of my 2.5 years of experience are highly relevant to my current position so as you guys said I think it's worth discussing it. Thanks !

Just to make thinks more complicated: you mean TVöd-Bund, yes? There are other TVöd as well.

I think so, to be precise it says "TV EntgO Bund" in my contract.

The degree associated with a certain payment group only serves as orientation/minimal requirement. It is also usually coupled to the position within the funding, so even if they wanted to offer you a higher group there, they couldn't. Roughly speaking, if they hired you e.g. as an untrained lab assistant, then you get paid as an untrained lab assistant, even if you also happen to have a PhD in the same field.
What is different is the level (Stufe) within each group. This only depends on work experience relevant to the job. So if your work experience was in something similar to what you are hired for now, this may be worth discussing with your boss.

Thanks for the explanation. My previous experience is in the exact same field and pretty relevant to this position. I was not employed in Germany though, would that be an issue ? I will discuss it during our next meeting anyway.

@qwetzal If you have not been employed in the "Öffentliche Dienst" before (basically in a position that pays according to TVöD), the worthiness of previous work experience (an with it the level into which you are sorted), even if done in Germany, is something that you need to negotiate with your future employer. Normally the levels work like this: after one year you switch to level 2, after 2 more to level 3 and so on.

@qwetzal Normally the country does not matter. Depending on whoever makes the decisions in the bureaucracy, the result may vary though. I've heard everything, from people getting their PhD-time (in a different country) counted as work experience for a post-doc position to "only work in the precisely same position in the same federal state can be counted"...

@mlk Thank you guys, I will have a chat with my employer, I have a good relation with them and they are honest people so hopefully they can push in my direction. Will edit this post with the answer I got.


text:
Concerted efforts to digitalize MS and PhD theses?
Some universities digitize their masters and doctoral theses already and some of them even share them for free online. However, there are enough that are not digitized that inter-library loan remains often necessary for literature searches. Obviously this is just a matter of time until the librarians catch up with the modern era. However, government funding is often involved and could be leveraged towards progress in those instances. Has this been proposed already? Have similar mechanisms been proposed?

I am not sure I understand your question. In my EU country, in most universities I know of, you already upload an electronic copy of BSc, MSc or PHD thesis and anyone is allowed to download them for free (unless the author puts an embargo period on his thesis).

I would like to second @Alexandros's comment and ask for clarification in particular with respect to the word "digitize". What is the original way these theses are created, if not digitally, on a computer? Are they written with a mechanical typewriter, or even handwritten?

Are you talking about digitizing theses from past years, when computer-readable manuscripts were unknown?

Without clarification from the OP, I assume this means projects to take classic dead-tree manuscripts to digital formats through a method like scanning. Google did/does it with books, so I think the question is if this is being done or has been proposed for theses and dissertations.

Has this been proposed already? Have similar mechanisms been proposed?

Yes, it was proposed, this was even implemented, partially or at a whole country level. In Russia, for example, all PhD theses' copies are centrally stored, scanned and recognized (if there wasn't already a digital copy) as a part of the degree awarding process. From 2005 the process needed a digital copy submitting, also, most of the old theses(~1.5M total) from 1951 were scanned.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
Could writing a survey of the state-of-the-art in my field compensate for my lack of publications?
I am applying to graduate school for a Ph.D. program in the field of business and management. The professor I want to work with endorsed my application, but warned that getting admitted might be difficult because
the Ph.D. program is rigorous and I have no publications in the field. Would it help if I showed my basic research knowledge and skills by reviewing literature and/or writing a summary of his research projects? Would this strengthen my application for the Ph.D. program?

(I don't know anything about business school, nor much about academia outside of the US)
Three reactions:

Professors never have enough time. I seriously doubt they would be eager to read a long, unsolicited report from someone they don't know.
Further, a "review" type publication is not really research experience --  understanding a concept is different than being able to come up with and test new ideas.
On the other hand, if you do know what you're doing, a "review" of a certain field can be a nice service to the community. You could write it and submit it to a journal/conference or just post it on the arXiv. Then you would have a publication that you can list (depending on your timeline).

Assuming the goal is to get admitted, the best thing to do would be to get some research experience by working for a professor. If that's impossible, and if you really are qualified to do so, writing a "review" for publication couldn't hurt -- though it's a lot of work and will likely have only a little impact.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
When writing a review article, do I need to compare to or discuss limitations of previous review or primary articles on the topic?
I have been told that having a published review article is viewed as an advantage for higher education. The topic of my review article is the impact of information technology on the practice of human resource management.
What are the sequential steps that I have to follow to make my review article acceptable to renowned publishers and university graduate professors?
I have heard that to make a review article acceptable, the first task is to read vastly about the subject related topic of review article. After reading extensively, how can I proceed - I mean how can make linkage of writing in every parts of review article?
Is it necessary to make a comparison, to find a gap or limitations,similarity of the previous primary and review articles on the same topic in writing my review article?
Do I have to present the information about what others authors have done or shown? or do I have to include my own thought, ideas, decisions along with the other scholars?and if I intend take some information,graph from copyrighted books,will I need to take permission from real authors or just paraphrase the information on own my words?

Certainly, a good literature review paper (also frequently referred to as a survey paper) would be an attractive asset in anyone's academic portfolio. However, it is easier said then done. Some people on this site even replied to one of my related questions that such papers is more of a prerogative of senior, very experienced scholars. Perhaps, there is some truth to it; likely, a lot of truth. However, I do think that those opinions should not prevent people from attempting to write a good survey paper (or do anything, for that matter). The reason for that, in my opinion, is twofold:
1) Self-assessment. Some people are better at carrying out specific research, while others are better at synthesizing information, in general, and results of research studies, in particular. We often do not know our strengths (and weaknesses), unless we try both types of research.
2) Self-improvement. In my opinion, it is very important to set the bar high, so that, even if we do not achieve a desired level of success for particular goal today, we will learn much more, thus, increasing our chances for succeeding in that goal and in general tomorrow. Plus, even, if we will not be have our survey papers published in a respectable outlet any time soon (while that IMHO should be one of the goals), we will obtain a much better understanding of the whole field of study (or subject domain) as well as gain an excellent practice in academic writing, which is very valuable on its own ("practice makes perfect").
Now, I will cover briefly some specific aspects that are related to your question. Firstly, we need to understand that there are no ready-to-use, step-by-step recipes for success, in general, and for writing good survey papers, in particular. Sure, there are some guidelines and templates (which are indeed helpful), however, the quality of a survey paper is IMHO dependent on a variety of factors beyond those aids, such as a potential author's knowledge and understanding of a field of study, research stream of subject domain, their abilities for writing clearly and concisely as well as constructing a mental picture of a large body of research and synthesizing from it, often creating rather complex conceptual frameworks. There exist papers that demonstrate how to do that; in particular, for example, for the information systems discipline, see papers by Webster & Watson (2002) as well as by Levy & Ellis (2006). However, again, it is one thing to see how something should be done and another to do that yourself. Another issue that we need to be aware of is that approaches for writing survey papers are quite different across disciplines and/or fields of study.
Secondly, we need to understand that writing a survey paper is not the same as performing a systematic review or a meta-analysis (by the way, they are often confused as similar things, which they are not, despite some overlap). We need to understand that meta-analysis is a statistical toolset, whereas systematic review is a type of study, which might or might not use the former.
Thirdly, depending on the discipline or field of study (as well as on the author and journal/editor), a survey paper might be closer in its form to a narrative review, rather than a systematic one. While systematic reviews are often regarded as more rigorous approach to covering a topic, they are not without their shortcomings. In fact, good narrative reviews might be as valuable, if not more, to science and research community, as their systematic counterparts. See this paper for some details.

References
Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature review in support of information systems research. Informing Science, 9, 181-212. Retrieved from http://www.inform.nu/Articles/Vol9/V9p181-212Levy99.pdf
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), 13-23. Retrieved from https://web.njit.edu/~egan/Writing_A_Literature_Review.pdf

A narrative review and a systematic review aren't mutually exclusive - systematic is about how you approach the evidence, narrative is about how you present your findings (as opposed to eg meta-analytic)

@user2956063: In general, I agree with your point. However, that is not always true: one, who knows a specific field of study and the corresponding research landscape very well, can craft a narrative review without formally performing a systematic literature review. In other words, while a narrative review can (and usually does) contain some systematic approach to reviewing literature, it might be so informal that using the term "systematic" does not make much sense in those cases.

@AleksandrBlekh sorry but you didn't really answer the question. The OP asked a very specific question, I think your answer provides interesting info but it's a bit too broad.

@user4050: Thank you for your kind words. I'm not claiming that my answer is the perfect or even a comprehensive one. However, it seems that the OP was happy with it, since it was accepted. Also, remember that, in addition to a specific question in the title, there are other questions in the question's details, which might have been addressed in my answer. Having said that, feel free to offer a better answer and I will be happy to upvote it. :-)

Take a look at the PRISMA statement - http://www.prisma-statement.org - It's very biomedical - so may not directly apply in your area - but I think that most of the guidance it gives can be generically applied.
You need to be clear about what you intend to write. In the medical world there is a clear difference between 'reviews' where an author picks papers he likes to support a particular point of view, and 'systematic reviews' where the literature as a whole is analysed in a systematic repeatable way. The former (reviews) essentially have no academic value or credibility beyond sometimes being useful for assisting the learning of undergraduates. The kudos lies with systematic reviews.
I'd certainly expect a review article to discuss previous reviews in the same area - pointing out their strengths and weaknesses, and making clear what the new review adds... Because if it doesn't add anything what was the point of you writing it or me reading it?
I'd also expect to see your opinions on the evidence you've reviewed - but a good review will source and present that evidence in such a way (eg systematically) that the reader can decide for themselves whether they agree with you, rather than having to take your opinions and analysis on trust.


text:
On balance, is it more valuable to review for a journal you haven't yet reviewed a paper for?
I'm a grad student, and over the weekend, received two review requests from very good journals in my field, one of which I have already reviewed for.
I declined one because it was out of my area, but it made me wonder if, all else being equal I should prioritize reviewing for a journal I haven't yet, or if it doesn't matter.
I have often seen CVs in my field list journals reviewed for, as well as a number of reviews, so it is information that is out there and valued

It would seem to me to be a very minor issue. The quality of your record will be based on other things.

@Buffy Well, what about from a learning to peer review standpoint?

From a learning to peer review standpoint, both seem useless if you don't get any feedback on how well you did.

You should prioritize reviewing for the editor you want to impress.

@AlexanderWoo You should make that an answer

I suggest that's in no way a minor issue as everyone should be able to see who viewed this from an outsider's angle…
All other things being equal, would it be better or worse to have reviewed for more, or for fewer journals?

@RobbieGoodwin - In most research communities, there are no outsiders that matter.

Sorry, Alexander. Did you truly not realise that in every community, viewpoints make a difference?
If you really can't see your own speciality from the theoretical viewpoint of an imaginary outsider, how is that fault mine… or anyone else's?

Multiple reviews for a journal indicates that you are a trustworthy
reviewer.
Reviews for various journals indicates that you are known across your
discipline.

Thus, both seem to be positive signals (for different traits). No need to prioritize one aspect over the other.
(By the way, you can have such peer review activities verified at Publons, which may be better than just listing them on your CV.)

https://academia.stackexchange.com/questions/94541/publons-is-it-a-serious-thing/148049#148049

Opinions on Publons vary.

I am assuming you are not in a field with professional editors - that editors of journals are generally professors.
In this situation, journals aren't really entities that you have reputations with.  You have reputations with the editors of the journals.  These are the people who could have an impact on your career.
Good editors remember who writes timely and useful reviews for them - they have to in order to continue to pick good reviewers.  It's natural that their impressions of their reviewers carry over when they need to make decisions on whom to choose for other purposes.
So, assuming you are going to be a good reviewer, prioritize reviewing for the editors you want to impress.

There is no downside in accepting to review an article from any reputable journal: a good review can only improve the quality of the submission and of the journal.
It is equally essential to decline if one is not comfortable with the manuscript, or one does not have the time to do an adequate job at the review, precisely because a bad review for any reason and for any journal may allow marginal or poor manuscripts to be published.

This doesn't answer the question. Maybe the missing part is "Assuming I only have time to do one good review."