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Can one submit to two places BUT one is non archival?
I was wondering if one can submit to two conferences/journals, but one is non-archival (e.g., a workshop). My curiosity stems from the NeurIPS experiment showing that the submission process seems highly stochastic, so if the submission does pass the non-archival compliance, I wanted it to go to a workshop at least that is non-archival e.g.

Publication
Accepted papers and supplementary material will be made available on the workshop website. However, these do not constitute archival publications, and no formal workshop proceedings will be made public, meaning contributors are free to publish their work in archival journals or conferences.

So, is it ethical to submit nearly the same paper twice, but one is archival, and the other is not?
Also, what would one do if it gets accepted by both?

Context
Fyi, I know two submissions to archival places are wrong, e.g., I submitted a paper to two journals, and the first published it, but I agreed to publish in the other; what can I do? but it's confusing given non-archival places like AI workshops.

There are almost certainly norms in your field about this.  Ask someone in your field.

My did several variants of a theme, so perhaps if you change something or extend it (somewhat like what Buffy's answer says) it will work out beter.

The whole "paper count" idea as a way of assessing peoples' work leads to this (to my mind bogus) constraint of not being allowed to tell a story more than once... and, gosh, maybe it's a story that isn't over, is ongoing, etc? "Talking about one's current progress" is surely the desired thing... as opposed to having it be disallowed because you'd already talked about it? After all, big-shots get to tell their conjectures more than once, eh? :)

@AlexanderWoo I did and they said parallel submission to workshops are fine but of course details lie in the devils for call for papers e.g. CVPR doesn't allow it while ICLR NeurIPS workshop doesn't care. The NeurIPS workshop even explicitly says it's fine. There are already some great answers. :)

This is fairly common, in my experience, at top machine learning (and adjacent) conferences. Usually, the non-archival venue is a workshop attached to some conference, so the repeat submission typically happens in one of the following ways:

Early version of paper is submitted to a non-archival workshop A, and then to an archival venue B (e.g. a conference). This could also happen if the paper was first rejected at the main track of the conference which A is attached to.
Paper is accepted at main track of a conference (archival), and then submitted to a workshop (non-archival) attached to the same conference, whose focus area is the subfield the paper belongs to. Some workshops may accept such papers without additional review.
A previously published paper at an archival venue is later submitted to a workshop that focuses on its subfield.

The idea behind additionally submitting to a non-archival workshop is typically to allow more detailed presentations (e.g. an oral presentation as compared to "just" a poster at a larger conference), more detailed discussions, more visibility for the paper, and to an audience that primarily is from the same subfield (as compared to a broader audience at large conferences).
I am not sure if submitting to an archival and non-archival venue simultaneously is common, but I do not see any ethical issues, beyond two primary considerations:

As other answers say, you should check the policies of the archival (and non-archival) venue. For example, NeurIPS allows submitting papers that have previously appeared in non-archival venues:

Work that has appeared in non-archival workshops, such as workshops at NeurIPS/ICML, may be submitted.

CVPR, on the other hand, does not:

A publication, for the purposes of this policy, is defined to be a written work longer than four pages (excluding references) that was submitted for review by peers for either acceptance or rejection, and, after review, was accepted. In particular, this definition of publication does not depend upon whether such an accepted written work appears in a formal proceedings or whether the organizers declare that such work “counts as a publication”.

Additionally, some venues may allow such submissions in general, but may restrict them during their review period.

If the submission to the non-archival venue is after the archival paper has been published (with copyright transfer), you might need to check the policies of the publisher regarding this.

If it gets accepted by both, you will get the chance to present at both. Formal proceedings and copyright transfer would only take place for the archival venue, and that version would typically be considered "canonical" when listing on publication lists and for citing the paper. In particular, assuming the two are nearly identical, you do not get to count them separately in your list of publications.
The question of submitting to arXiv is a orthogonal to this discussion, as arXiv is an archival "venue", more than most non-archival workshops, but by not being peer-reviewed, does not have the ethical concern of wasting reviewer time or questions regarding copyright transfer.

I think it's worth emphasizing that it seems CVPR doesn't allow workshop publications but it does allow arxiv "publications" (since it's not peer reviewed) -- since both are none archival. Thanks!

@CharlieParker I would actually say arXiv is archival, and particularly more than most non-archival workshops, since arXiv publications have a unique ID and are expected to be persistent, while non-archival workshops (at the most) upload the paper on their websites, with no guarantees that it will still be available in the future. This is why non-archival workshop papers are often cited using their arXiv identifiers. However, arXiv submissions for the most part are not considered when talking about dual submissions in CS, and the ethical argument is primarily about not wasting reviewer time.

The workshop clearly states that they are okay with people submitting their workshop submissions to another venue.
If you want to do that, ask the other venue for permission.  Show them the quote you showed us.  Then, if they are paying attention, they will grant you permission.
The main point is:  Don't waste people's time reading the same submission more than is necessary.

This may be a distinction without a meaning. The workshop is telling you that they make no claim, copyright or otherwise, on the material so won't bind your submissions elsewhere. However, it is a form of "publishing".
And if you submit to a journal it is their rules that will apply to whether it will be published there or not. Some journals won't publish things "published" elsewhere, though that is changing and has changed in some fields. If you submit to such a journal after "publishing" the same work elsewhere, they will be unhappy.
There are no laws (that I'm aware of) for such things, but the journals and conferences make their own rules.
It might be safer and more appropriate to submit the paper to a journal snd submit something different, but related to the conference workshop.
Workshops, being more informal, probably accept partial work and work in progress. The point is sharing ideas, not presenting finished work. That is my experience anyway. You can certainly talk about a paper at a workshop with no consequences.
Said another way, the conference gives you permission to publish elsewhere, but a journal may not. It isn't necessarily symmetric. Caution is advised.

You can, of course, check with any journal you propose submitting to. Contact the editor and/or read the submission guidelines. If they permit preprint publication (arXiv...) then it is likely they will permit what you would like to do. (likely, not certain)

For this specific NeurIPS workshop:

Can a paper be submitted to the workshop that is currently under review or will be under review at a conference during the review phase?

From our side, it is perfectly fine to submit a condensed version of a parallel conference submission if it is also fine for the conference in question. Our workshop does not have archival proceedings, and therefore parallel submissions of extended versions to other conferences are acceptable.


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Responsibility to report Health and Safety concerns
I'm a Ph.D. student who has recently become aware of several health and safety hazards in my lab, which I have been unable to address with my supervisor/other lab members. These hazards include alphabetical storage of chemicals (ignoring chemical compatibility), leakage/spillage of chemicals in storage, large quantities of waste being stored in the lab for years at a time, contaminated items being left unlabelled in fume hoods for years at a time among others (I don't want to be too specific in case it could be identifiable).
The university is aware of some of these issues (not all, and in my opinion, not the worst), but there has been little progress in remedying the problem. Undergraduates and postgraduates use this lab.
I feel I am responsible for reporting these issues to the Health and Safety department. Still, I'm worried about retribution from my supervisor - I wouldn't want to be fired for this. Additionally, if the lab is shut down, this is likely to have significant negative impacts on some other Ph.D. students working there.
What would you suggest I do? On the one hand, it is a health and safety hazard. On the other, I wouldn't want to risk my position and the other Ph.D. students working in the lab. The university is already aware of some of the issues. Should I report my concerns to the university?
Thank you.

Although you say that the university is aware of some of these issues, sometimes action on these matters depends on telling the right people.  In particular, if you have not yet told the relevant Health and Safety officers at the university then that might be the reason that no remedial action has been taken.  I recommend you report these matters to the Health and Safety office at the university immediately.  Indeed, if you have a look at the university policy, there is a good chance that it imposes an obligation on its workers to report safety issues to the Health and Safety office.
Shutting down a lab temporarily to deal with a safety hazard is an annoyance, but it is important to remedy safety problems and could save someone from being seriously injured or killed.  If the lab were to be shut permanently as a result of the issue, that would presumably reflect serious safety problems that present a danger to people, or repeated breaches of safety rules.  Either way, reporting the issue is appropriate and it is your responsibility to report.

I feel as though I have a responsibility to report these issues to the Health and Safety department, but I'm worried about retribution from my supervisor...

If that is the case then you have big problems --- any supervisor who would retaliate against a lab worker for reporting safety concerns to the university is a menace, and if you are in an environment where that is a possibility then it reflects serious dysfunction at your university.  Most occupational health and safety laws prohibit retaliation against workers for reporting safety issues, and in some jurisdictions retaliation against workers in these circumstances can even constitute a criminal offence.  In addition to protections in OHS laws, there are also specific provisions for whistleblowers in circumstances where workers suffer retaliation for reporting safety problems.

Thank you for this - I had a look at the Universities H&S policy and as you say there is an obligation to report to either your supervisor or to health & safety. Do you think I would be required to go via my supervisor? Ideally I'd like to go directly to H&S but I don't want to appear as though I'm going over their head, but also I don't want to risk any repercussions (not necessarily active retaliation, but more passive - like not reading papers, cancelling meetings etc - so harder to prove in terms of whistleblower protection).

As stated, I recommend you report these matters to the Health and Safety office at the university immediately.

if the lab is shut down this is likely to have significant negative
impacts on a number of other PhD students

Sure, if you leave the lab be managed as it is now, on the other hand, a good percentage of PhD students will think that it is ok to manage dangerous stuff careless and disrespecting rules, because they are (or they become) self-entitled doing more important things than respecting stupid bureaucracy on how to store plutonium and fluoridic acid. They are PhDs after all, expert-to-be on the subject, and they may even become professor, if all things fall in place.
What do you prefer, a negative impact now on them, or a negative impact on society allowing this way of thinking (rules are there for a reason, quite often to protect one from oneself, rather than from the others).
Do yourself a favour: you are a good guy that discovered a number of issues, anybody not complaining is already impacting negatively YOU and a number of honest people.
The same people running the lab this way, supposedly for the greater good of science and consequently of humanity and not for their personal benefit, are doing a disservice to the same humanity at large they claim they are working for.
And being the managers lab or users, they are actually doing a lot for their personal benefits.
So yes, report to the university, but first contact a lawyer.
You have to protect yourself and you want to report the violations in the most specific and bureaucratic way possible, please remember that you are going to act against the lab manager and therefore against the university, not for the university.
In the Netherlands they say:
hoge bomen vangen veel wind
Good luck and please remember that the best success is how at peace you are with yourself, not how many succesful papers you churned out while working in dangerous conditions (unless you have an enormous ego that can be at peace only by churning out humongous papers working in dangerous conditions :) ).
PS and side note: regarding safety culture in the UK, an interesting andeoctal point ... maybe there is a foundation acting in the name of John Cockcroft that may help/support you?

Depending on your location, there may be a union representing University staff - for example, in the UK it would be UCU for academic staff, Unite for technical support staff and Unison for clerical staff. Any of these would have specialised Health and Safety reps.
Importantly, unions are used to situations where the employer may not be acting entirely in their staff’s best interest (such as your observation that the University is aware of some violations but turning a blind eye) and will probably be more discreet and understanding of your conundrum. They may also have prior experience of dealing with similar situations and could give you an idea of what the University’s reaction would be depending on which action you choose.
Unions may not be able to act on your behalf unless you are a member, but in most places you should be able to at least speak informally with a rep.

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About Master's Thesis Literature Review - Is this plagiarism?
I am writing my thesis for political science in a Masters programme. I found another thesis that is extremely similar to mine, where I could literally use its literature review.
So, I wanted to ask if that would be considered plagiarism: if I followed and cited the same references the author mentioned, but of course with my own words through paraphrasing, would that be admissable?
As it is the literature review, and I am going to refer to all the papers he mentioned, would that be considered plagiarism? I aim to follow the same structure for the literature review as  well.

For purposes of plagiarism, the work and effort of collecting and presenting other sources counts as an intellectual effort. That compilation constitutes an idea; the structure also constitutes an idea. Review articles are basically just an extended literature review, and these are publishable papers in their own right.
Therefore, if you want to reuse that compilation of sources, you would need to cite both the secondary source that compiled the work as well as the original papers to not be credibly accused of plagiarism. That doesn't mean you need to seek out and cite every other paper that has cited some combination of original work that you use, but if you use a secondary source to identify what other sources to cite, it should be cited.
Now, as for assessing your thesis, even if you do not plagiarize (because you cite properly), your thesis may not be deemed sufficiently original if you derive such an important part of it entirely from someone else. Basically you have not shown that you can competently arrange this information yourself, so you may not be seen as having "passed" the thesis assignment.
I'd recommend changing your viewpoint on the importance of literature review; you seem to consider it a boring secondary task but it's actually a central part of research.

@Gantendo Indeed, borrowing/stealing a literature review is a  shortsighted approach that devalues the rest of the work done.

If you're merely using the lit review to identify sources, which you then independently read and analyze, then that's not plagiarism.
But if you're using the lit review's description of the sources, or analysis of them, or decision on how to organize them (e.g., which one to discuss first, which one to use second), or which ones to give more attention to vs. which ones to mention only in passing- in short, if you're taking from the lit review anything that represents thought by the lit review's author- then you absolutely need to cite the lit review. If you don't, it's plagiarism, full stop.
You shouldn't think of this in terms of "can I get away with not citing?" You should think in terms of "Did the lit review's author do ANYTHING original, including the choice of how to organize the material? And if so, how can I make sure that person gets the credit they deserve?".

You should definitely cite the previous thesis, and credit as appropriate the pieces of literature review you use. But you need to do your own work, get the original sources cited, check if anything newer has been published, if other points of view were left out. The purpose of doing a literature review is to check what has been done, what problems are open, learn applicable (or not) techniques to approach the problem.
You are left to either dig deeper than the other work or veer into a (slightly) different direction to make overlap less. Perhaps try a new approach to the same basic data, collect more data and see if the original approach still fits, see if the same approach is applicable in other, similar, cases.

Not everyone agrees with me on this, but I don't care: literature reviews can be kind of silly artifacts that we hang on to in many disciplines. I generally think they can be wastes of space, when a few will placed and described citations with relevant discussion will usually do. When I'm refereeing for top journals, I don't really care who you cite, I'm reading for the methods, coherence of the paper, discussion, and other things.
To be clear, I'm not saying you should never cite work related to your own. It's just not usually necessary to dedicate an entire section of your paper to do this. Where am I going with this?
Make your lit review short. You and another thesis did very similar work? Okay great! Cite their thesis if you'd wish, and discuss the relevant work from the citations you (likely) were going to use anyways. And then?
Move on to better things. The more important things. My thesis had a very very very short lit review, also for political science incidentally. Why was it short? I had other and more important things to discuss than what previous papers hypothesized, and anything beyond what I put would simply have been a waste of space.
My advice to you, is to hit the high points. Talk about only the literature that is quintessential to your question, methods, or subject. That way, you'll be more concise, you won't simply repeat another person who summarized the information well, and then you can get to what you should be doing anyways: talk about why your paper is unique, interesting, or of relevant to the broader field. That's what you should be focusing on, why your own original work matters. Other work may be necessary to this, but it won't be enough to convince them why your work pushes the proverbial envelope.

Hmmm. The advisor might have something to say about this. A literature review serves more than one purpose for a novice.

Yeah I'm not saying they're never useful, and I know it'll matter at different levels. I see some utility in them. I still use them in my work if I feel they're necessary for why my new stats method matters or if there's genuinely a lot of literature that actually warrants discussion. But that isn't always true.

I don't think this answer is helpful advice for a masters dissertation (indeed, it may be harmful to the student)

That's fine. I said not everyone agrees with me on this, and that's okay.There are peer reviewed articles in top stats/econometrics journals that just don't have lit review sections. My point is that lit reviews are not laws of nature, and they can be useful or not depending on context (I write systematic reviews, so again, I'm not against reviews in principle) and that they're not the life or death of your paper. Anyways, what is wrong with what I said? Why is it bad?

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Why did my papers get repeatedly put on the last day and the last session of a conference?
My paper got accepted normally (e.g., with neutral to positive reviewer rating) to a conference.
I have noticed that, I always get to present (oral or poster) on the last days of the conference, and worse, at the last session.
Predictably, I did not get a large audience, and got fewer questions/useful feedback compared to papers presented on the first, and second day of the conference.
Does that indicate (low) perception of my paper by the conference organizer?
It is almost as if they want to conceal my paper. The conferences are ranked typical B-level with ca. 50% acceptance rate.
What can I do?

Are you saying that this has happened multiple times, at different conferences? Are you suggesting that the organizers of the different conferences have conspired against you?

Is your name or surname starting with X/Y/Z? it may be a very unlucky crossfeedback between your session of choice and your name.

How many instances are we talking about? What is a B-level conference? What field are you in?

It may be nothing to do with the perceived quality of the paper, but more to do with the perceived level of interest in the subject matter, or of your personal "brand recognition". FWIW I once travelled half way across the world to a conference to find my slot moved to 8am on the last day after the conference dinner, and I had an audience of 7.

No, I did not mean "conceal" in conspiratorial way. More in the sense I suspect they think the paper is of low relevant, or perhaps quality (ouch!)  so it's only there for meeting quota. I rather not be specific about the conference name, but I have counted 4 instances so far (one of them put me on the last day, but not last slot, to be fair). The field is computer vision. My name is not on the last alphabet no. @Michael Kay, I'm just a second year phd student, so obviously I have no "personal brand" to speak of.

It's probably not your papers, but the general subject which gets pushed into late session.

Does your name begin in a letter near the end of the alphabet? It's not likely for conferences to order the presentations in alphabetical order, but it could be a very distinct possibility which we could clear up.

too short for an answer: as an organiser you want the last speaker to be a good speaker since the last talk will be remembered more. So it could also be some form of appreciation of your abilities as a speaker.

I have heard that at certain physics conferences, they put all the "crank" papers in a certain session, so the cranks are there talking to each other.

It is most likely chance. I've organized conferences, and the schedule is typically made by grouping accepted papers into topics, and laying out the topics over the available sessions, without paying attention to who wrote the papers or their score. The score is often used to decide between oral and poster (which I think is silly, because the score is not related to how suitable the paper is for either), but I have never seen anyone put into the last session out of spite.

How many times has this happened? Is it multiple instances of the same conference, or multiple completely different conferences, or a mix?

To expand on @MichaelKay's and your own points, is it possible that your paper could be categorized as being of relatively low relevance to the organizers and/or audience of conf? Is your work is too arcane or abstract relative to the conference’s ideal attendee? You may or may not already do this - consider optimizing your paper's abstract and/or contents for this "ideal attendee," if you're not already doing it. Consider dumbing it down a bit if needed, compare your paper to those of the first speakers to make note of any diffs
"If you don't like how the table is set, turn over the table"

"The field is computer vision." - how much is there to say about image recognition software that isn't already extant and proprietary? What were the other fields that beat you out? AI, eleven G, and cluster satellites? IR was hot stuff... back when we were fighting a cold war. Now my phone can do it, so....

@Christian: At the conferences I know, many people already have left on the last day.

@Mazura : there can be new problems and novel solutions even in computer vision. I worked on a project specializing in detecting forgeries, especially where parts of different photos were placed together in the same image. The field itself is not new, but as better methods arise in making forgeries harder to detect, so is the field of detecting them advancing.

Your question has just made me doubt any conference I have presented at - I have been in the last session so many, many times and I always considered it just bad luck (getting a talk in the first place was the exiting bit).
While some of the other answers have already given you a nice perspective regarding the considerations that many conference organizers may have had when they were putting together the schedule, let me also offer my perspective, having now organized multiple different conferences and symposia, big and small: in none of these cases have we put together the schedule based on whether we considered talks selected from abstracts to have lower or higher impact or significance. As an organizer, I want my last session to be as strong as the first, if possible, because nobody wants an empty room towards the end of the conference (what has happened therefore: putting an awesome keynote speaker or exciting invited talk at the end to make sure nobody heads home).
Even when picking best speaker awards I've often been in last minute hushed discussions as we considered a talk from one of the last sections without a coffee break in between the session and the actual award ceremony.
I do agree that it sucks to be on the last day because you lack the free conference wide exposure you might get by presenting on the first day, but there are other ways to put yourself out there: sit with people you don't know at breakfast/lunch/dinner, be active and introduce yourself to people at poster sessions etc. etc.
So, don't get worked up over it, don't read too much into it and just focus on the contents of your work and on genuinely getting to know people in your field.

Thank you for the encouraging explanation. I did try to speak to people with similar sub-topic in the first/second day of  the conference (i.e., after their oral or during their poster session). Sometimes
I casually bringing up my works, and if I'm lucky I get the feedback that I need. A lot of them were already gone by the third day! So to whoever shares  the same fate as me, this is the least you can do. :)

"none of these cases have we put together the schedule based on talks we considered to have lower or higher impact" seems to contradict with "the opposite has happened: ..."

+1 for the clustering by topic, and the odds and ends/not enough talks to fill one whole session. This has been my experience as well. Sometimes to the detriment of the clustered sessions as no 'new' people would attend those.

@DanM. well spotted, edited for clarity.

How do conferences group papers?
(Based on my experience and therefore subjective)
Usually, the programming committee has a meeting to discuss referee reports. On rare occasion, last-minute reviews are being done by PC members to balance the fact that reviewers did not respond timely or in order to understand the review reports better. Before the meeting, the PC chair might divide the submissions into three groups: definitely accept, where the PC is expected to rubber-stamp this decision, definitely do not accept, for instance, because none of the three reviewers voted for a straight accept, or as desk-rejects, and the papers that ought to be discussed. In parallel, the number of papers to be selected is determined.
When the work is done, the Chair with or without help groups papers together by threads. Some papers will cluster naturally, and sometimes they will not, leading to "Odds and Ends" sessions. After the sessions have been determined, they get assigned to dates in the calendar. Even if there is no Best Paper award, the papers that according to the PC and the reviewers are or were candidates, have their session placed in the prime time, usually a session after the key-note talk. Everything else gets a spot assigned usually without much deliberation. Odds-and-ends sessions without a strong paper and without strong cohesion tend to get assigned last session in a day.
Now, big conferences like VLDB and those where the tracks are more independently organized work differently.
TO ANSWER YOUR QUESTION:
It is unlikely that someone is out to get you, because the decision making process is too distributed for that to work easily. It might be a sequence of bad luck (statistically slumps are likely to occur) or it might be that your work does not cluster well with other work.
Unfortunately, the conference method of disseminating information has draw-backs, and you are suffering from one. If you look at the history, conferences started out much more as an exchange of ideas before the developed into the main dissemination method of Computer Science.
If conferences accept your papers, someone thinks your work is worth-while. However, even if you end up presenting in a prime spot, your audience tends to be already overwhelmed by the program and you might not get a substantially better reception.

No, no, no. I didn't mean "get me" in such conspiratorial way. No one care that much about some unknown phd student, haha. More like, in your words my paper was more than often perceived as belonging to "Odds-and-ends session". If that's true, I'm trying to improve my works and would like to know where to start.

Working on stuff that is a bit out of the main stream is actually cool. So, with "odds-and-ends" I do not mean bad or inferior quality or "just squeaked by". I remember one PC meeting where the PC was quite enthusiastic about accepting a paper because it was a bit different, even though it fit the call-for-papers.

I very much doubt that the organizers want to conceal your paper. If they thought that they would have rejected it.
I suspect that although it cleared the cutoff point for acceptance, the organizers thought it less interesting or more specialized than papers scheduled earlier. You might be able to infer that by looking at the rest of the schedule.
As for what to do? Approach other participants whose work touches yours. You may learn something; that may encourage them to come to your talk. Try harder to connect your work to questions people at that conference care about.
That said, congratulations on the acceptances.

I hope so, by conceal I mean in the "hope no one noticed I put this paper here" sense. It is true that my subtopic is more relatively specialized. Yes, I have been approaching people on the first and second days, because often by the third day they were gone!

Mostly, my experience agrees with the existing answers by BioBrains and Thomas Schwarz — schedules are decided by a variety of concerns, with “quality” far down on the list if it’s considered at all.
The one common exception, in my experience, is that committees often aim to put strong talks in certain key time-slots — speakers we expect to give a compelling talk and/or attract a large audience (which mostly coincide). These slots typically include each day’s opening talk, especially the first day, but also often each closing talk as well, especially the last day’s, to make sure the conference ends on a strong note, and also to motivate participants to stick around to the end.  An unpopular schedule on the last afternoon is a recipe for audience attrition.
So a few of the strongest (maybe c.10% of all talks) often have their quality taken into account in their scheduling; but for the placement of the rest, quality is essentially irrelevant to the schedule.  In particular, I’ve never known a committee speak in terms of trying to “bury” bad talks, or consistently put weaker talks into later sessions; so I wouldn’t draw any bad conclusions from the scheduling you describe.

It seems very unlikely that there is a conspiracy to bury your results at the end of conferences. I think there are a few possibilities:

Your work was considered less interesting and therefore not worth a "prime-time" spot. It's impossible for anyone here to determine this and, honestly, its not worth thinking about.
The conference organized posters/presentations based on some other factor like order of submission, name of author, topic, etc... Anything could have landed your presentation at the end - someone has to be last and it might not have much to do with quality.
Related to 1, are you sure your presentation was really best suited to a particular conference? Its always possible that your work is actually quite good but not 100% on topic (or of broad interest).
Bad luck. Unless you have a trend spanning dozens of conferences, maybe there is nothing here. We tend to focus on extreme outcomes and you're more likely to notice when you are at the end (especially since you think this is a trend) than when you are stuck somewhere in the middle.

I don't actually think there is anything for you to do. It's not even clear that there is something wrong in the first place. You probably can't go wrong with @Ethan Bolker's suggestions. But really, just focus on producing quality work and identifying conferences which are best suited to sharing that work.

Ah again, I did not mean "someone is out to bury my works". More along the lines "well, I need to fill some sessions, this paper got peer-review acceptance, but is a bit dubious, if I put it here, I bet no one will notice". #3 is plausible reason for some of the conferences I attend, but definitely not all. Yeah, I will try my best to focus on the content.


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Is it OK to generate parts of a research paper using a large language model such as ChatGPT?
Large language models (LLMs) are complex neural networks that can capture and represent world knowledge present in the human-generated text as their weights. LLMs are trained on a large collection of textual documents; for example, billions of webpages that are crawled from the web by the Common Crawl project's web crawler. Moreover, an application of LLMs is to generate text in response to a query which can be a set of keywords, a question, or even a simple sentence.
For example, the conversation in the image below is generated by an LLM called ChatGPT, created by OpenAI (a startup company working on LLMs).

The text in the box with a slightly darker background is generated by ChatGPT in response to the question on the first line.
As you can see, the generated response is similar (or even indistinguishable) to a reply that a human might have given to the question on the first line.
Now, my question is: is it OK to generate parts of a research paper, for instance, background or parts of the introduction section, using an LLM such as ChatGPT? Naturally, I would give appropriate credit to the creators of the model.

What is your goal that you hope to accomplish by using AI output in a paper? Are you hoping to write a paper in less time without loss of quality? Are there some things you're not sure how to write about? I personally can't figure out what you would gain from this.

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OpenAi is a startup?

There are some aspects of academic writing that you need to consider if you want to use AI-generated text passages. I'll address four of them in my answer: Licensing, Plagiarism, Argumentation, Referencing.
Licensing issues
Language models are trained on source texts, and these source texts are typically harvested in great volumes from publications (online or print) without much concern for the license under which the text was published. Thus, the input for the language model may containmaterial that is not licensed for this type of processing. Consequently, the status of the model and of the work generated by the model is a legal gray area. Here's an article that discusses this "legal minefield" for AI-generated images, some of which are based on the OpenAI software. Much of the discussion applies directly to AI-generated texts.
Accusations of plagiarism
To my understanding, AI-generated content depends on associations between training data and classification data. Input data might be for instance a collection of photographs together with a classification matrix. This matrix expresses whether each image contains e.g. a car, a bicycle, a staircase, a zebra crossing, a traffic sign etc. Training an AI basically means to develop a mathematical model that expresses the learned associations between the images and the classifications. The resulting model can make a prediction whether an image that was not part of the training data also shows a bicycle. In this way, the model functions as a classifier. But you could also reverse the model logic by asking the model to generate an image that contains a bicycle. The AI model will use the learned associations between image information and classification to produce an AI-generated image of a bicycle.
A potential problem in this process may arise from data scarcity. If the training data contains only very few images of a bicycle, the generated image will be very similar to the input. This means that if e.g. the prompt for your text generator is highly specific, the output text may be very similar to the few input texts that the AI can draw from to generate the text. This is called AI plagiarism.
So, the AI-generated text may be a direct plagiarism of another published text. But to you as the end user, there is no way of knowing how similar it is, and there's no way of checking the actual texts that the model used to generate the text based on your prompt. This means that if you publish an article that contains AI plagiarism, and if somebody realizes the similarity between your article and other published material, they can rightfully charge you with plagiarism. Your only excuse would be that you didn't write that section, the AI did. But in an academic context, this will still ruin your academic reputation.
Argumentation
Academic writing depends on coherent and cohesive argumentative elaboration. While AIs have made huge progress in that regard, this is still something that needs to be checked very carefully. AI models don't understand the argumentation of an input text in any sense that relates to human understanding. At best, an AI model generalizes over argumentative patterns recurring in several input texts, and it uses these generalizations to create a text that resembles these argumentative patterns. It is your task as an author to ensure that the generated argument is indeed valid. You can only do this if you know the subject matter well enough – but you can ask an AI model to generate a text for a topic that you're completely ignorant of. Much responsibility is required at this point.
Your example text illustrates this. To me as someone who isn't an expert in reinforcement learning, the description sounds reasonable. But I can't judge whether it's actually correct. I would never dare to use this passage without reading up on the difference in detail so that I can verify the passage. But then, I could just as well write the section myself.
Referencing
One rule that every first-year student should learn is that academic writing needs proper referencing. If you make claims, you should back up the claims by references. If you report previous work, you need to provide sources.
However, an AI model doesn't know about referencing. It may learn that after particular text patterns other typical text patters occur. So an AI may learn that sentences from the academic genre very often end in sequences like (ABCDE 1234). because very often, the input texts will provide references for a statement in a sentence at the end of the sentence, and the format of this reference is often (Author Year). However, the AI doesn't know what this pattern means. It doesn't know that you can't use the most probable letter-number sequence here, but that it has to refer to an existing source. Obviously, this is less than useless – it's actively invalidating the academic quality of your text.
Alternatively, the AI may not produce any reference, which is also unacceptable and requires your intervention as the author. The generated text in your example doesn't contain references – but it contains many claims for which I as a reviewer would ask for sources to back up the claims. Again, it would be your task as the author to find sources for each of these claims. And since the AI didn't use sources in any sense of term "source" as used in academic contexts, it may even be impossible to provide appropriate references for an AI-generated text that satisfies the very strict standards for referencing.
Summary
AI-generated texts are an interesting development, but it's a topic that is so novel that there are many unresolved issues (both legal and ethical), and also many aspects that ask for a high degree of responsibility and knowledge from the author.
My assessment may change in the future, but as of today I'd advise against using AI-generated texts in academic writing.

An anecdote about the plagiarism aspect: I asked ChatGPT a fairly specific question copied directly from stack overflow. The question was originally posted a while ago, so I assume it is likely in the training set for ChatGPT. The response I got was a correct answer that was the same conceptual solution as the human answer posted on stack overflow, but was clearly "written" from scratch; the structure and wording of the response was not close to the human answer.

An alternate potentially relevant example of the plagiarism aspect would be the currently ongoing class action lawsuit about GitHub Copilot. The exact context is a bit different (programming as opposed to scientific writing), but the core premise of the suit is arguably broadly applicable to most types of AI trained on data harvested without being licensed for that purpose.

"However, an AI model doesn't know about referencing" On last night's The WAN Show (Youtube), one of the hosts actually was able to get the ChatGPT to generate documents with extremely convincing MLA style references to websites. It linked to real and relevant websites, but not necessarily a real page; someone grading/reviewing would have to manually check each reference to know it wasn't real. And that's with OpenAI currently having a bunch of restrictions in place that might be hamstringing its ability to generate more authentic references.

TLDR; it already does know what references are, and how to include them in documents with proper formatting. It's only a matter of time until all the references actually link to valid and relevant sources.

@anjama: I'm pretty sure the answer's point is that things like chatGPT aren't actually understanding that referencing the real sources of the information used in the text they generate. They're just learning that sentences/claims in such texts are often in the style of This is a claim (Source 1996). So it can certainly generate "convincing" references, but that doesn't make them accurate ones – even if the linked page actually exists, that doesn't mean the source supports the claim it's being cited for.

I tried a version of this, by feeding part of an academic chapter I'm writing in the humanities to GPT-3 to complete. It invented an extremely plausible academic at a real university to cite. It took me a comparatively long time to convince myself that the person didn't exist, and neither did their book.

I'd say two of the raised points should be fairly easy to address by AI models: a) the referencing: while that's AFAIK not what GPT-3 is good for, google's serch engine does use an natural language processing model (https://en.wikipedia.org/wiki/BERT_(language_model)), so such models do exist. (Even if the initial text generating model would produce "claim (ref123)" patterns, that could fairlay easily used for post-processing for finding literature) b) plagiarism checks: software to check for text similarity does exist already.

Oh, and in particular some sections like materials and methods or ethics and acknowledgements typically use a language that is very formalized (and for some I'd argue that I'd like to see even more formalized content there, as in check lists for reporting standards). Those could be auto-generated from very simple templates, and I wouldn't see anything wrong with that (maybe we could go for icons like with the various CC licenses?)

Another possible issue is authorship credit. You can't list a bot as an author, but you can't claim authorship of that passage either since you didn't write it.  The bot's creators didn't write it either.  When your journal requires the signatures of all of the authors in order to publish, how are you going to handle that?

Re licensing: I should note that also everything humans write is generated by training on a large number of books. lectures and other copyrighted sources and learning to produce something similar but original. It's hard to draw a line between the original re-elaboration of existing content by a human and what an AI does. I'm sure someone in philosophy could write a thesis on this topic.

If your academic work has to pass through peer review, then I suspect that the referneces will be a big problem: The referees know the references that should appear. If there is one I don't know, I will look it up. I am interested in this stuff, that is why i am an expert and why i was asked to referee. If you give me a reference I have not heard of before, then I am excited. If it turns out it is bogus, and more problems turn up then I am going to have a conversation with the editor, who may have a conversation with your advisor or employer...

A little additional reading: https://arxiv.org/abs/2107.06751

No, it is not acceptable to use a language model such as ChatGPT to generate parts of a research paper. Research papers are expected to be the original work of the author(s), and using a language model to generate part of the paper would be considered a form of plagiarism. Additionally, research papers require a high level of accuracy and attention to detail, and a language model may not be able to provide the level of accuracy and precision that is needed in a research paper. It is always best to do your own research and write your own paper.

This is the answer given by ChatGPT.
It doesn't recommend using itself for generating a research paper.

This answer made me laugh

@edelweiss: I'm reminded of the tape recorder classroom scene in the 1985 movie "Real Genius".

This answer is also useful in that it will enter the data future text generators will compose their answers of :-). As an aside, this shows that there can be self-reinforcing drift in generated content. It's easy to imagine a dystopia where most people just listen to, reinforce and replicate machine generated prejudice and nonsense.

+1 for ... everything. Thanks. I personally bow to our new overlords.

+1 I know this answer goes against so many rules of this site but whatever.

@edelweiss Not me. It's been overdone so many times.

I wish accuracy and detail were the only substantive issues. The answer created by chatgpt is quite euphemistic.

I think, right now, it remains fairly dangerous to do so, at least without extensive oversight. I'd apply similar logic to other software tools that have been around much longer, like grammar or spellchecking software - it's okay to use the software to suggest or assist the author writing, but at the end of the day the author needs to be able to take responsibility for what is written and make the decisions.
LLM AI-generated text has gotten quite good at mimicking real human writing, but it isn't capable of doing the sorts of logical checks that humans need to do when they write. I'm especially worried about writers who use these tools because they do not feel confident in their own writing ability, particularly in a language they're less comfortable in. They may know their topic, but not understand the generated writing well enough to verify it.
Remember that research is meant to be somewhat novel; an AI can only be trained on what already exists. It might generate good summaries of topics that have been summarized many times before, but that doesn't mean it's equipped to actually synthesize knowledge in a forward-looking way. In my experience reading examples so far, I've certainly seen some impressive language constructions, but they feel to me a lot more like a novice university student trying to smash together an essay at the last minute, especially one trying to inflate their word count to get as close as possible to some instructor-mandated length. They have the essay feel down, but not the content. The example you posted seems representative of that - the first sentence, even:

The main difference between (adjective) (noun) and (noun) is (adjective)

Whoa, that's like, profound. Note that none of what is written elaborates on what it means to be "deep" in the context of reinforcement learning. It would be absolutely worthless to have any of this in a paper introduction. Even if the text were better, without citations to where the work came from, you have to admit that you're just trusting an AI's compilation of some library - that's not the same as you personally reading and trusting and vouching for the information. It's not merely the creators of the tool that need to be referenced, but the creators of the knowledge.
As others have mentioned, I do think disclosure and credit is important, but right now I think there are more serious issues to be concerned with.

A potentially useful answer (+1) but I'm not sure it's possible to engage in "extensive oversight" as a user of these AI models.  The main issue is that, as a mere user, you don't have access to the training data to "oversee" what was done in the algorithm.  Consequently, I would think that any oversight that is attempted would still be unable to solve the problems raised in the other answers.

@Ben I agree with you that I'm not sure it's possible; I feel like most people who are interested in using this technology aren't really imagining any oversight at all, so I figured that would be enough to warn them off. An example of "extensive oversight" that could actually work might be using an AI-generated paragraph to remind yourself "oh yeah, I should talk about (this thing that I normally take for granted but my audience might not)" - I don't think that's what most people think of when thinking of this technology, but it might be a more valid use.

To clarify, I don't mean to refer to "oversight" as in the generation process, but in the product, the same way one would oversee spelling or grammar check recommendations from software.

Fair enough.  I think the danger pointed out in other answers is that you could still end up essentially plagiarising other material without realising it (if that material went into the AI training data) and your proposed oversight of the product would not alert you to that.

Upvoted for "that's like, profound" :-).

Actually it's more profound than your irony suggests. Notice ChatGPT says the difference is “the use of deep learning”. It might seem redundant to say deep learning is used in reinforcement learning, but that's in fact the crucial bit: deep RL is not a deeper version of RL, but instead combines together two learning technologies. I agree that the whole is still a pretty lame description, but it's certainly no mere tautology either.

@leftaroundabout It fails to describe any of that, though. The text is only useful if you already know what deep learning is, in which case you likely don't need to read any of this background.

This is the best answer, because it shows that so-called AI can't reason. That's the actual problem, compared to which plagiarism is an afterthought.

#GivingCreditstoChatGPT is an act of supporting plagiarism!
I am not familiar with legal/copyright issues, so I will not address them here.
Inspite of its capability to generate well-structured "informative" text, ChatGPT is far from being intelligent (and consistent). It is very dangerous to blindly rely on this tool.
For example:
What is p-value?

A p-value is a statistical measure that is used to determine the likelihood that a given result occurred by chance if the null hypothesis were true. If the p-value is low, it suggests that the null hypothesis is unlikely to be true. On the other hand, if the p-value is high, it suggests that the null hypothesis is likely to be true.

Another answer for "What is p-value?"

A p-value is a statistical measure which is used to indicate the probability of a given hypothesis being true. It is calulated by dividing the observed results of a test by the expected value of the test. The lower the p-value, the more likely the hypothesis to be true.

The best answer (the 4-th try)

A p-value is a numerical value used in statistical hypothesis testing that represents the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

If I have a student, and he/she keeps giving me a different answer (ranging from far from being correct to very close) everytime  ask him/her the question "What is p-value?", I would say that this student just scrapes the entire internet, and gives me whatever the first result that he/she finds "useful" without knowing what it is. That is the problem of ChatGPT. It does not know. It does not understand. AI is not intelligent.
Also, that also implies that it is hard for others to locate the source that you get information from. The answer you get for asking the question above on ChatGPT is not fixed. Eventually, it will be impossible to do that.
Last but not least, in my opinion, ChatGPT could be an useful tool for productivity, and there is no reason why you should not use it if you found it helpful for your work as long as you acknowledge its shortcomings. Don't trust it blindly.

The danger of a "great tool for productivity" is producing what? Definitely a risk of quantity over quality here.

You don't know how tremendously happy I am to know someone else thinks the same thing. AI is in general over-glorified instead of being seen for what it actually is. It is not even remotely intelligent and does not, in any case, take into account or understand context. Without taking into account context, it cannot possibly be called intelligent.

@JW I edited it. Not what I intended to say, but I still believe many people will find such a tool useful. Quality-control is needed, just as you need to do quality-control for things you find on Google. It is important to acknowledge what it can do and cannot do. However, what makes me feel uncomfortable is that we don't know the source which this tool gets the information from.

@NordineLotfi I often use the term "statistical learning" instead of "machine learning". It is obvious. Many "machine learning" techniques were invented by statisticians before computers even existed. Also, is there any machine learning model which does not rely on an implicit assumption about probability distribution? I doubt that. Machine learning also does not "learn" anything from the data. It is inherently based on statistical models which are by no means intelligent.

The more you talk, the more I relate to and agree with what you say. I said the exact same thing once in an argument/discussion, but mine was probably more direct because I said "AI is just statistics", but now, I feel like it's closer to "brute-forcing with statistics" instead.

@JW it can be a great tool for productivity like many other tools: cutting short certain steps. You can easily go from bullet points to a first textual draft with an AI tool. It probably will be neat to generate visualisations ad-hoc e.g. in LaTex etc. Of course you would still need to proofread, adjust it or restructure it - either directly or through AI tools again (likely a mix). A lot of people find it a lot easier to iterate over a first text than to start from scratch. Yes there are risks, if you cannot wield that tool or want to misuse it.

It's worth mentioning that OpenAI expressly forbids using their API to generate content without clear attribution including how the AI was used. (Note: this is applicable at least to the OpenAI API Platform, and may be applicable to other language models, though it is unclear if it applies to chatGPT.) This is in addition to the ethical and legal issues on the publication side that the other answers discuss.
From OpenAI's website (emphasis mine):

Content co-authored with the OpenAI API policy
Creators who wish to publish their first-party written content (e.g., a book, compendium of short stories) created in part with the OpenAI API are permitted to do so under the following conditions:

The published content is attributed to your name or company.

The role of AI in formulating the content is clearly disclosed in a way that no reader could possibly miss, and that a typical reader would find sufficiently easy to understand.
....

For instance, one must detail in a Foreword or Introduction (or some place similar) the relative roles of drafting, editing, etc. People should not represent API-generated content as being wholly generated by a human or wholly generated by an AI, and it is a human who must take ultimate responsibility for the content being published.

The website also has a suggested blurb that can be used:

The author generated this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

I suspect that other large language models will have similar use policies.
In short: it's not just up to the venue and the authors. By using a large language model published by some company, you are obligated to read and follow the terms of use for that model, which may include proper attribution.

You are citing from the content policy of OpenAI's API, but the OP's question was about ChatGPT, which is a different product.

@ChristophThiede That's interesting, I assumed this would also apply to chatGPT, but I don't know. The general terms of use doesn't mention attribution.

Also, in the brand guidelines, they say "If publishing text or images generated by an OpenAI model, you are not required to attribute."

As long as your text is clear and your research is sound, you may use any tool you prefer to aid you in the writing.
Indeed you'll have to proofread the text and make sure that everything is correct.
You'd have to check if the license of the particular model you wish to use permits you to use it in such a way or if there are some limitations/attributions required.
If you believe that the text generated by such models is good enough, I don't see why you should not use it.
EDIT: as pointed out by other people these kinds of models may have licensing issues with the data used to train them, this is indeed a big problem. Also as pointed out by others, these models may plagiarize other authors if they have little information about a topic.
This said, I stand by the idea that if the final text is a publishable text, with a sound research and that satisfies all the requirements to be publishable, you may use any tool you prefer to write it.

Whoever downvoted this might explain the vote. It would be useful information for the OP.

(+1) for answer. @Buffy: When reading the question I was thinking along the lines of this answer, but I did feel a bit uneasy about my thinking. Nonetheless, and this was still before I read stanton63's answer, I couldn't come up with any counterargument that wouldn't also (at least in times past) argue against things like spell check, use of calculators (to say nothing of computer algebra systems), online translators (naturally subject to additional human editing, at least at the present time), etc. Or going further back in time, dictionaries, math tables, mechanical drawing devices, etc.

What if my preferred tool is Cut and Paste from an unreferenced source? Spell checkers and grammar checkers are pointing out possible issues, and I override them all the time since I know better. Wholesale production of bulk text is a different beast.

I was not the first downvoter, but I have just added a downvote (-1).  The assertion that you may use any tool you prefer as long as you are clear and your research is sound seems obviously incorrect to me (and very dangerous advice).  It ignores all the serious issues of licensing/plagiarism, etc. that are set out in the main answer.  To go further, what if you instead use the "tool" of copying another researcher's paper without credit/citation?  May you do that so long as the text is clear?

@Jon Custer: It seems I read between the lines a bit when reading the OP's question "is it OK to generate parts of a research paper", because I didn't think this was LITERALLY what the OP was asking, but rather the question was intended to be about the use of this as a tool for helping smooth out English written by a non-native speaker, and whether this is cheating in some way. I assumed the use would be along the lines of the user, for example, looking at how certain phrases appear and maybe making use of these phrases in their own writing, (continued)

at least if the generated phrases seemed to better express what is intended. That is, I didn't think the OP was actually asking about copying word-for-word the output. However, this is the interpretation that most everyone here has, and in looking back over the OP's question, perhaps that interpretation is what the OP actually intended. Personally, I can't imagine using such a tool to write about anything the least bit nontrivial without expecting to find all sorts of "not quite right" statements (or misleading statements, or ambiguous statements, etc.). (continued)

Indeed, examples of (what could be) such "not quite right" statements are given in my answer about a similar situation. Incidentally, note that Buffy's comment there also seems to take the view that "no one" would use the output literally without careful reflection -- Mindless application would seem to be foolish enough not to be considered.

@Ben "It ignores all the serious issues of licensing/plagiarism"—most humans do not come up with original ideas by themselves, but rather by referencing a culmination of other sources. Why would you view the work of a chatbot differently? Is it simply because a chatbot has access to a wider dataset? If anything, that should make a AI generated text more unlike any single source text.

It's difficult to give a conclusive answer. AI-assisted writing is fairly new, and the legal and ethical understanding of it is in flux. I would caution against using AI-assisted writing in published work for a few reasons related to plagiarism and copyright.
AI tools are trained on large datasets, often including published, copyrighted material. You can cite the tool itself, but what about the publications it learned from? Should those be cited as well? Are you certain that it won't produce a paragraph similar enough to a former work to constitute plagiarism? Do authors have a responsibility to ensure that the tools they use aren't plagiarizing or infringing on copyright, or does that responsibility lie solely with the developer of the tools?
At the moment, there are no clear legal answers. It would be prudent to wait until AI technology—and legal and ethical opinions of it—mature.

The legality is really the least important issue. The actual problem is that so-called AI can't do what its proponents advertise: Reason intelligently. It blindly puts together text fragments based on statistical patterns in the (huge) training data.

The example in the question body generates the impression that large chunks of text are to be generated automatically and submitted without editing. However, I'd like to approach the question as asked in the title and more from the perspective of what I consider acceptable usage of AI models as tools.
Summary

I think such AIs can be valuable tools to help generating initial draft texts, once the current hype is over and they get to be used for what they are good for. Though under much closer human supervision than the question envisions: These models are giant autocompletion engines, and could/should be used as such; they are not trained to predict reality or truth (other than the reality of what may occur in a text).
We are already using deep models - possibly without realizing - for several tasks during research and paper writing without particular concerns.
Whether we find it acceptable or not to use such tools to help with proper scientific writing, we'll anyways have to learn how to deal with auto-generated spam papers.

TLDR
First of all, I'm in STEM (for this question: as opposed to sciences/disciplines that are more working directly on language such as literature or also law). The science I generate is partly experimental and partly computational (as in crunching the numbers of those experiments). Fields whose core is in the language may need to develop their own standards.
(Also, I'm into machine learning, but not into natural language processing.)
However, keep in mind that we are already using automated tools:

Automated theorem proving in maths

Google uses a natural language processing model in their search engine.

Translators such as deepl come to mind as well. Particularly in case you're not a native English speaker and look for translations of sentences explaining your thoughts in your native language.

Spell checkers are state of the art. Thesauruses or synonym and antonym dictionaries have been around since ever, nowadays there are also rephrasing tools. Newer tools like grammarly were explicitly recommended in a scientific writing course I lately attended.  Note that grammarly can rewrite sentences to make them shorter and more legigible.

I happily use tab completion, including the variety that predicts what word I want to write next when writing a text.

Similarly, speech (voice-to-text) or character recognition (OCR) software uses deep learning.

At my secondary affiliation JKI, colleagues develop cadima for helping with systematic reviews. Behind the scenes, they work with (and on) natural language processing AI models for that tool as well.

AFAIK, using such tools is not considered questionable.
As a side note: when I worked on my very first paper, I was at an institute that still had a secretary/assistant. She drew the illustrations about our methodology according to our instructions and got properly acknowledged similar to how one would acknowledge who helped with field or lab work, but did not contribute intellectually to a paper.  There is nothing ethically questionable in this practice, neither.
I'd tend to "acknowledge" models like other software relevant for the paper: either in the text or as reference if there's a scientific publication about it. Since that is done not only for, say, the package that computes a statistical model but also for packages like ggplot2 that produce diagrams and figures, why not for packages that generate (parts/early drafts) of the text.

The aforementioned scientific writing course discussed a work flow that has distinct steps for deciding which content, text generation (and recommended freewriting, possibly using voice-to-text software) followed by editing, revising (and reorganizing) the produced text chunks.
GPT-3 and similar models predict how a given text continues.  This is IMHO important to understand:
It's not answering questions in the sense we'd discuss question and answer, for those models, answers are text that is predicted to come after a question.
It also means that some behaviours that may be considered less desirable are actually what follows from the precise prediction task of those models.
In particular, since the "answers" are in a sense extrapolated from the initial text/keywords, we should expect that this process is unstable (see the different "explanations" of p-values in @edelweiss' answer). (I also wouldn't be astonished if this extrapolation behaviour turns out to be related to the possibility of the model generating abusive or heavily biased language: such extrapolations have a tendency to amplify things)
I think we also need to keep in mind that when talking about writing a scientific paper, we're talking about novel (as in new), but real (not fictional) content. This means we're asking for even more extrapolation: what we want to write about must be outside the body of text known to the model.
And, GPT-3 is not a domain-specific model for predicting how a scientific text goes on, its training base comprises all kinds of text, including invented ones such as fictional or even fantasy texts.  Note that the AI generated content has often been described as dream-like - which may be how we humans perceive such instability in the prediction. (It would be interesting whether such a model trained purely on scientific texts and textbooks would produce less fantasy)
(We also should not wonder about "fake news" being produced; and it should by its set-up be able to predict also how fake news stories go on.)
This suggests to me that these models may best be used as very sophisticated auto-completion tools (with tight control by us of the content - and that possibly tighter control is needed for generating/auto-completing parts of a scientific text than for, say, a web page or blog post on some well-known topic), and maybe as a tool for the free writing stage, generating (even a variety of) sentences or paragraphs from maybe a list of bullet points.
I'd gladly use an AI tool with a voice-to-text of my talk as starting point, applying a "scientific paper" language style to generate an initial pre-draft version of a manuscript. ...*dream*...
When writing a scientific paper, we provide (and are responsible for) the knowledge content. I care less about "who" puts the thoughts into nice-to-read English, and personally find the use of also very sophisticated tools entirely acceptable.

relevant blog posts and papers

Artificial intelligence is getting better at writing, and universities should worry about plagiarism

Using Internet based paraphrasing tools: Original work, patchwriting or facilitated plagiarism?
I'd expect the observed indicators (which are mostly rather clumsy) to vanish rather soon as the tools become more sophisticated

In an AI world we need to teach students how to work with robot writers

OK computer: to prevent students cheating with AI text-generators, we should bring them into the classroom

‘Tortured phrases’ give away fabricated research papers  and the original Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals
From the nature news (haven't yet read the arXiv preprint) it is not clear to me whether spam papers were newly generated (by AI) or whether plagiarism was tried to hide behind automated forth-and-back translation.

(One of these posts mentions that also plagiarism detection software will become more sophisticated, establishing "writing fingerprints" of people. Unless these fingerprints are transparently computed, and based on proper science, I'd have the same type of concerns with plagiarism accusations on such a basis that I have about predictive policing, in particular based on intransparent (and likely not well validated) models.)

I don't want to speak (much) on the licensing issues:

If ChatGPT is found to have been trained on material that was not properly licensed (see @AustinHemmelgarn's comment on the respective lawsuit) it will likely be taken down. A different AI model trained on a legally available body of text will likely be developed (possibly by someone else) and provide similar functionality - so the rest of these answers is not affected.

As Schmuddi discusses, copyright of content generated by AI is not clear.

However, I'd expect for an AI model (software) that's used basically as autocompletion tool, i.e. under close (intellectual) supervision of humans, that the copyright lies with those humans. This is IMHO the more relevant and realistic scenario for a proper scientific text being written using an AI.
(Consider: if Microsoft were to loose a lawsuit for copyright infringement for something incorporated in the Word software. Whatever happens to the future of Word, your text produced with the help of Word stays yours.)

An interesting and highly related question over on Law SE today: https://law.stackexchange.com/questions/86936/chatgpt-is-reproducing-unlicensed-code-from-my-website-verbatim-what-can-i-do-a

@JonCuster: yes, though that refers (also) to a different "level" of plagiarism, i.e. ChatGPT was likely trained on data that was obtained illegally. (see AustinHemmelgarn's comment above about the law suit against OpenAI/Github) Whether identical code produced by an AI that was provably not trained on that material (which is extremely unlikely here) is plagiarism is an interesting question. AFAIK, in IP right, the possibility of two humans accidentally producing the same IP content is acknowledged, and not considered an infringement. Academic rules are yet different, since more literature

... search is required. Whereas e.g. in patent right, a inventor may keep their invention secret (but prove the date, e.g. with a notary) and when later someone else files a patent with that claim, the first inventor can go on using their previous knowledge. AFAIK, they are not required to screen patent applications whether their secret invention shows up anywhere. Whereas the authors of a paper are expected to make sure their state of the art is up to date at submission.

And this: https://meta.stackexchange.com/questions/384410/how-often-does-chatgpt-give-an-incorrect-answer-to-an-se-question

An answer to the literal question in the title, "Is it OK to generate parts of a research paper using a large language model such as ChatGPT?", is probably yes. At least if your research paper happens to be about LLMs, trying to understand how and why they work and how to improve them.
A fairly recent and somewhat extreme example is this article, mostly written by GPT-3. The (co)authors asked the LLM to write a paper about itself (using multiple queries). As a consequence, that paper contains a lot of sections written entirely by the LLM.
Whether or not something like that really is acceptable (or should be) is, just as mentioned in the other answers, still up for debate. This particular paper is still in peer-review (or it was at least in June 2022, when the (co)author wrote an opinion piece on their own paper and the ethical and moral questions it poses in Scientific American).

I'll add to the other answers that some conferences explicitly forbid some use of language models, so if you plan to use some language models you should make sure it doesn't violate some policy of your publication target.
E.g., https://icml.cc/Conferences/2023/llm-policy:

The Large Language Model (LLM) policy for ICML 2023 prohibits text produced entirely by LLMs (i.e., “generated”).  This does not prohibit authors from using LLMs for editing or polishing author-written text.

Times have changed. These days the answer is broadly "yes", but 1) you can't list the AI as an author, 2) you must disclose where you used AI, and 3) you are still responsible for the content of your paper, even if it was written by AI.

The use of artificial intelligence (AI) tools such as ChatGPT or Large Language Models in research publications is expanding rapidly. COPE joins organisations, such as WAME and the JAMA Network among others, to state that AI tools cannot be listed as an author of a paper.
AI tools cannot meet the requirements for authorship as they cannot take responsibility for the submitted work. As non-legal entities, they cannot assert the presence or absence of conflicts of interest nor manage copyright and license agreements.
Authors who use AI tools in the writing of a manuscript, production of images or graphical elements of the paper, or in the collection and analysis of data, must be transparent in disclosing in the Materials and Methods (or similar section) of the paper how the AI tool was used and which tool was used. Authors are fully responsible for the content of their manuscript, even those parts produced by an AI tool, and are thus liable for any breach of publication ethics.

No, using language models like ChatGPT to generate parts of your research paper is not advisable. Several issues arise from the direct usage of artificial intelligence services such as OpenAI ChatGPT, BingAI, BardAI, and so on in the education sector. Some of the common issues are:
1. Plagiarism Accusations,
2. Licensing Issues,
3. Referencing Issues,
4. Originality of the Sources Issues
If you are confused about any concepts, you can ask these services to explain the meaning. However, after grasping the concepts, you should do your own research, learn from it, and use it in your research papers.
One more thing. Someone once said, "In your life, all kinds of troubles will come looking for you all the time. You don't need to go out and look for them."
A legal team in New York City recently got reprimanded by a Federal Judge for writing a legal brief with the help of ChatGPT. Turns out, none of these cases that ChatGPT had cited to the lawyers actually existed.
NYC judge scolds lawyer for using ChatGPT to write brief full of ‘legal gibberish’; attorney swears robot ‘duped’ him | Yahoo! News | Friday, June 9, 2023
To conclude, I think that it is okay to use ChatGPT within ethical and code of honesty constraints, but beyond that, you are going to attract all kinds of negative attention and consequences you never expected to receive.
Good Luck to you.


text:
Is it advisable to get copyright on a research proposal and an academic article before submitting them?
Is it advisable to get copyright on a research proposal and an academic article before submitting them?
Do you think there are some distinctions between the two cases mentioned (a research proposal and a scientific paper).
I refer to legislations of Western countries, mainly.

What do you mean by "get copyright"? Could you indicate your country, please, maybe along with a brief description what the term "get copyright" entails in your jurisdiction, based upon your understanding?

Sorry, I am not English mother tongue. I am from Italy.

I have added the appropriate country-tag to your question. The WP article on copyright in Italy suggests that "Italian law does not require any copyright formalities such as registration or deposit for copyright to subsist. The Civil Code (Art. 2576) and Law no. 633 (Art. 6) provide that the rights are first acquired upon creation of the work as a particular expression of the intellectual effort." I am not posting this as an answer, though, as I think an answer should refer to (and translate from) the primary source rather ...

... than relying only on a shortened translation of the original legislation, and one found on Wikipedia at that. There are various Italian regulars on Academia SE, maybe one of them will write an answer.

But I am not applying in Italy, but I guess it is the same everywhere, as suggested by Nicole Hamilton

Well, then please specify the countries that you are referring to in your question. I don't think this question can be answered (at least without any "probablies" and "pretty muchs") without looking specifically at single countries. (And if it is very dissimilar countries, you might want to consider splitting up your question into several individual questions.) Removed the [tag:Italy] tag again.

but I guess that in each country in Europe works pretty much the same for this. Am I wrong, in your opinion?

I am not sure. I suppose parts of these laws have been harmonized by EU regulations, but articles such as this one or this map of different freedom of panorama laws in Europe seem to suggest interpretation of copyright rules is not always uniform across EU countries, either.

A copyright exists on your original work from the moment it is created and fixed in a tangible form.  No marking or registration is needed.
Here in the US, this may be helpful:  Copyright in General
(In 1989, the US became a party to the Berne Convention, now followed by 171 countries.  So the law here should be similar to where you live.  But check your government's site or with an IP attorney if a lot is at stake.)

Thank you! I am European, however and I am going to apply to European institutions, while it may happen that I will submit papers to American journals, who knows.

The US now follows international conventions (we didn't always) so the rules should be pretty close where you live.  But check your own government's site or, if necessary, with an IP attorney if a lot is at stake.

It's worth noting that some journals will require you to sign over some of your copyright privileges as a prerequisite for publication.

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


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How to properly acknowledge an old famous mathematician in my article?
I am a Ph.D. student in mathematics. I have worked on a project and wrote a paper. A reputed old mathematician, who is not my supervisor but is an excellent and friendly human, in my opinion, consistently helped me to write the paper. I am about to submit the paper to a journal. When I offered authorship, the mathematician politely said that giving acknowledgment (in the acknowledgment section) in the paper would be enough.
So I have decided to acknowledge. But I am also thinking of dedicating to the mathematician's birthday on the paper's front page.
Is it normal to dedicate a paper to the birthday of someone who contributed to it? Or should I mention the contribution in the acknowledgment part?

@DanielHatton What kind of online banking grants access just knowing a birthday? That makes no sense. Birthdays are quite commonly publicly known dates. I cannot imagine any security system using it as a secret. If your bank does this, I strongly suggest you find yourself a better one!

@CrisLuengo True, but people also have a tendency to use their birthdays (or their families' birthdays) as passwords and PINs.

@DanielHatton: As far as I’ve ever seen, a dedication to someone’s birthday means something like “…to XX YY, on the occasion of her 70th birthday”, not “…to XX YY, for her 70th birthday on 29 February 2022” — besides which, it seems pretty unlikely OP would know more about the mentor’s birthday than is already public.

You could do worse than follow Einstein's example in On the Electrodynamics of Moving Bodies: "In conclusion I wish to say that in working at the problem here dealt with I have had the loyal assistance of my friend and colleague M. Besso, and that I am indebted to him for several valuable suggestions."

I think paper "dedications" are rare. A statement in the ack portion would be more common and probably sufficient. I've dedicated books to such people, though after they died, however.
But "I want to thank X for many many fruitful discussions on the material here and their guidance when things got hard." is pretty strong. Add another sentence about what a wonderful person they are and it will be over the top.
Note that in math it is common that advisors who contribute a lot to dissertations don't normally expect co-authorship or special thanks. "Just part of the job."

Thank you very much for deciding my mind. It is very helpful. However that mathematician is not my advisor but more than my real advisor

Agree completely with this. This is someone who isn’t your advisor treating you like you’re their PhD student, which is generous and deserves warm gratitude, but isn’t that unusual. I was lucky enough that both Lenstra and Jones gave me this treatment, Here’s what I wrote in your circumstances. Now I try to “pay it forward” with how I treat my colleagues’ PhD students.

@NoahSnyder, thanks for the paper.

@NoahSnyder OT but curious: in math papers, you don't have a separate section for acknowledgements? In physics, it's always a separate one at the end of the paper, and containing all personal and funding/institutional acknowledgments.

@Neinstein In my experience it's usually a subsection at the end of the introduction, but I guess it could be different outside of my bubble.

Agree with @DenisNardin. Not sure why I didn’t do it that way here, either because I lacked experience, or because it didn’t have subsections.

There's the aspect of field etiquette, that Buffy and Ben have already addressed: it's common to express these thanks in the "acknowledgements" section of the paper, which exists precisely for this reason. It's uncommon to "dedicate" a paper, except for unusual circumstances (like Buffy, I have only seen this done for the death of a mentor) and I have never seen an example of a "birthday dedication". I would personally find such a dedication odd and perhaps suspect that the junior researcher is trying to emphasise their connection with the old, respected mentor for personal exposure.
But there's a more relevant aspect which applies to you specifically: you have asked this mathematician how they would like to be recognised for their help, by offering them authorship, and they have specified their preference, which is to be mentioned in the acknowledgement section. If you wish to express your gratitude to this person, the best way is always to respect their explicit wishes, rather than override them.

60th birthday dedications aren't that unusual, but are usually for papers published in proceedings attached to 60th birthday conferences. The other circumstance where it's not unusual is a mathematician who has recently passed away. Other than those specific scenarios, yes, dedication is unusual.

An acknowledgement is a perfectly good way to show your gratitude to this mathematician, particularly if he does not want coauthorship.  It would be odd to dedicate a paper to the birthday of another mathematician, if only because birthdays for adults aren't really a big deal anyway, and they have nothing to do with the process of research.  I recommend you write a nice acknowledgement and get an experienced scholar to read it to make sure it is sufficiently flattering without going too far overboard.

if only because birthdays for adults aren't really a big deal anyway - some of us even hate them with passion :)

@WoJ: ...while some of us love them even having lived tens of them, so that's irrelevant. :)

@DaG: the first ones were fine, and it is after living, as you write, tens of them (it started at two tens, actually, many tens ago) that I really try to hide on the [redacted] of [redacted]

Is it normal to dedicate a paper to the birthday of someone who contributed to it?

It is neither normal nor welcome (or acceptable).

Or should I mention the contribution in the acknowledgment part?

A friend of mine turned around my PhD over a dinner. She came out with a brilliant idea that triggered a new field of research (this was a fresh, promising topic that was not applied yet to my area of research). While her comment was maybe 10 seconds long, it influenced a lot the direction I decided to take.
I was extremely grateful to her and put her first in the acknowledgment section, before the advisor, staff, wife, parents, and dog. This was the mark of respect that is expected, nothing more, nothing less.
Please do not make him feel weird.


text:
answering questions in a PhD proposal template
I have received an email from a university in the Netherlands in which the professor asked four candidates, including me, to write a proposal. In that proposal, I am asked to answer some questions. one question that I should answer is:

Statement of a provisional timetable

I do not know how to answer this request. Please help me with this.

Hi Mohamad! Would you please clarify what is exactly not clear about the question? Are you applying for a PhD position?

A "provisional timetable" is an estimate, hopefully informed, about how long the work is expected to take. It can be detailed, breaking down stages of the work, or not. Detailed estimates are probably better.
The timetable is provisional, however, since research results can't be scheduled. If you are dealing with the unknown, it is impossible, in general, to say in advance when it will become known.

So, is only writing that "hopefully it will take 4 years" sufficient to answer this question?

@MohamadGhoreishi You should try to give more information with more confidence than that. As Buffy says, you want to break down stages of your work in as much detail as possible (at least year-by-year but finer-grained if possible). Everyone understands these are estimates and can change, so don't feel you are locking yourself into anything. But the point is to show that you've thought through what your research will look like over the next 4 years. Writing one sentence like "hopefully I'll finish my work in 4 years" will not convey a positive impression.


text:
Research motivated by referee assignment
I was recently asked to review a paper. I liked the paper a lot and eventually recommended it for publication after minor revisions. The paper inspired me to do further research on some questions motivated by the paper. I did the research parallel to writing the report and quickly made progress (theoretical field, i.e., no experiments, etc.) I have now returned my report, and my research should be submitted/preprint-ready in 2-3 weeks.
Ethically, I think there is no problem. There is a preprint version of the paper that I refereed, so I did not use "insider knowledge" to advance my research. Also, the problems that I tackled were substantial and separate from the original paper, so there is nothing to ask the authors to add to their paper.
However, I am now pondering the timing of submitting/disseminating the preprint of my work. Should I (morally speaking) wait until the original paper is published? Until it is accepted (editor's decision still outstanding)? Also, should I submit to the same venue, or will this be met with disfavour?

I don't think they address your specific timing question, but there have been some related questions here, e.g. How to use results/ideas from a paper I reviewed?, A manuscript I refereed gave me an idea for a paper, not sure how to proceed, During review, I found a superior solution. What now?, What to do when I can prove a conjecture of a paper I'm peer reviewing

Thanks! Some differences of my question to the related ones: (a) In my case my evaluation of the original paper was positive with recommendation to accept (b) I did not answer a conjecture/question directly raised in the original paper, but started work 'building on' the original paper (c) a preprint version of the original paper is publicly available.

Also, should I submit to the same venue or will this be met with disfavour?

Submitting to the same venue may actually have some salutary effects here, insofar as it makes acceptance of the other person's paper all the more likely.  If you write to the editor to say that you are doing your own paper building on this work, and will be citing that work, then it acts as evidence of immediate impact to the previous paper.  I see no reason that would be met with disfavour --- surely an editor would be thrilled to see that a paper submitted to their journal (and perhaps being published) is generating interest that leads to follow-up papers and citations.

Also, submitting to the same venue might allow for better visibility of the final published work, since the editors may decide to put both papers in the same issue if there aren't huge delays in the review process.

In principle, you should have judged the paper under review and limit your brain to that. Unfortunately, we are humans and our brain does not work in tight compartment, so there was some crosspollination between your current work and the work under review (please note: working on the same issue made your review even more valuable, so you actually provided a good service to the authors and their work under review).
An honest way of proceeding would be to delay your pre-print or paper submission.
Why?
You worked on questions that were triggered by the paper under review. How long did you work on them? let's say 67 days. Now you have just to wait the publication of the paper you reviewed, read the paper, wait 67 days more, submit your manuscript for peer review.
This way may look inefficient, but the only reason you feel guilty, is because you had privileged access to the paper (and you were "forced" to look at it with great attention because of your peer-reviewer role). Removing the priviliged access, your work is ... normal science: your goal is the expansion (or the confutation :D) of someone else work.

downvoters are welcome to comment

I think the "wait 67 days advice" takes a strange view of academic ethics.  It seems to suggest that academic work is a contest or race that must be conducted under "fair and equal conditions" in order to be valid.  But in what way is academic research actually regulated to ensure that everyone is working under equal conditions?  Some people have more resources / time / abilities / insightful colleagues / etc. than others.

Also: the OP has already done the work, so they should wait to see if someone else can do the same thing?  If I were working on the same problem as someone else, I would want to know as soon as possible about the work they had already done, not after enough time has elapsed to give me "a sporting chance," because I want to do work that is actually new, not just work that I think is new.  I think the only real ethical obligation here is to the authors of the paper being refereed.   The OP cannot try to "scoop" the authors or proceed in any way without acknowledging their priority.