I think the professional sciences has, for a long time, been a social game of building ones career but it does feel like it's metastasized into something that's swallowed academia.
From the first article in the series [0]:
> Insiders ... understand that a research paper serves ... in increasing importance ... Currency, An advertisement, Brand marketing ... in contrast to what outsiders .. believe, which is ... to share a novel discovery with the world in a detailed report.
I can believe it's absolutely true. And yikes.
Other than the brutal contempt, TFA looks like pretty good advice.
A secondary and less visible consequence of this is that many people don’t go into academia in the first place because they are put off by the publishing system. And so many people that would otherwise be contributing to human knowledge are working in an office somewhere helping a random company sell more widgets.
Contributing to humanity’s knowledge is MUCH easier in the private sector than in academia.
In the private sector you can choose your patrons and your dissemination mechanism. Many, many scientists publish papers, publish code, give talks, write blogs, and otherwise distribute technical details about their work product.
In academia the Federal Government is your only serious patron and you must disseminate in academic journals/conferences, which generally do a piss poor job of providing incentives for either doing good work or communicating well about that work.
Any time I hire a junior PhD I have to UNDO a ton of academic writing/provlem-solving propaganda and reteach both common sense and normal writing style.
The harsh truth is that private sector scientists tend to do better science and disseminate it in more useful and lasting ways. They are paid better for it.
The academic scientists who are up to private sector standards tend to have diverse funding mechanisms and therefore rely far less heavily on prestige publication for their labs revenue stream. But most professors must publish papers because they are unable to do good work and/or communicate the value of that work to anyone other than their inner circle of friends (who sit on the grant review panels or take stints at federal agencies).
Without permission, yeah. But many companies do publish scientific papers. In both worlds, there's usually a game of publishing enough to give people confidence that the results are good, without actually giving away enough details to actually lose any competitive advantage (this is perhaps even more so in academia). In basically every field there will be things that everyone talks about and things that no-one talks about, and the latter is often even more important (but usually more boring know-how type things).
NDAs are standard even in small businesses. Any company developing technology will require them for technical staff because not doing so can cause the company to lose protection for its IP, even if none of the staff actually leak any technical secrets. Patent applications can be invalidated on the ground that the technology had been disclosed to individuals outside of an NDA, and trade secret protections forfeited for not taking reasonable precautions.
I have only worked with one business that did not require NDAs, and that was because it was built around an open-source sharing philosophy. Every other client, even very small organic-growth businesses and pre-seed startups, required an NDA, and if they hadn't I would have advised them that they should.
I think it's actually the opposite. American universities receive less research funding from external private sources than universities in the European countries I'm familiar with. The difference is probably due to the culture of charitable donations. Europe has a tradition of private foundations funding arts and sciences, while Americans make donations to universities.
In 2021, academic R&D spending in the US was ~$90 billion (https://ncses.nsf.gov/pubs/nsb202326/funding-sources-of-acad...). Out of that, 55% came from the federal government, 25% from the institutions themselves, 6% from nonprofits, 6% from businesses, 5% from state and local governments, and 3% from other sources. The share of businesses looks normal, while the share of nonprofits seems low.
You’re making a comparison then quoting only one side of that comparison, which is deeply confusing.
I’m pretty damn sure you’re wrong about Europe on a relative basis. The percentages in most of Europe are MUCH higher. Eg Germany is closer to 80% than 50% gov funded.
(Earmarked gifts to an endowment with some level of direction/advice vs a foundation is a real cultural and tax policy difference, but the end effect is what matters and that’s not as simple as you’re suggesting.)
And not to be too flippant, but the question about the world outside of America applies also to the world outside the West ;)
I didn't provide numbers for the other side, as the numbers you can easily find are almost certainly not comparable. International comparisons require a lot of work if you want to do them properly, as there are too many differences in institutions and accounting principles.
We could take the University of Helsinki (Finland) as a singular example. 56% of the external research funding comes from the national government and 14% from the EU. 16% is from private foundations, 9% from businesses, and the remaining 5% from other sources. The 16% figure from foundations is lower than it would be under the American model, as many private grants (particularly fellowships for PhD students) are awarded directly to the individual and therefore not included in the figures for the university. Overall, 70% of the external research funding is from the government, 25% from private institutions, and 5% from other sources.
I didn't include the share of research funding from the university itself, because I don't know what is included under it by American standards. If you adjust the American figures to exclude that, you get 80% from the government, 16% from private institutions, and 4% from other sources.
It's good to remember that Europe is not a continent of social democratic welfare states but a continent of warmongers and old money that happens to be quiet for the moment. A lot of that old money went into foundations that fund prestigious things such as arts and science. European private donors don't like funding education, as they consider it a government responsibility. American donors on the other hand often give money to universities, which then use it primarily for education, buildings, and infrastructure.
Only when they are disseminating in an academic venue. Most non-university research dissemination happens outside of academic venues.
And even then usually only because it’s expected, not because it was actually useful. (And no, it’s not because academics are more ethical about acknowledging the shoulders they stand on. Academics rarely cite the chip designs, software libraries, lab instruments, instruction documents, training materials, etc. What “counts” as something that deserves a citation mostly boils down to “did you publish it in a venue controlled by other academics”, not “how important was this to enabling your contributions?”)
The fortunate thing about the private sector is that you don’t have to spend years of your life shaping opinion on citation ethics, because people are using your stuff instead of half-interestedly saying that they may’ve skimmed the intro to a pdf describing your stuff. And if people use your stuff and get value from it you can usually extract some of the value that creates. Which means you don’t need vanity metrics to convince some government agency to throw you some coin.
I don't find this true at all in my experience, you and I apparently have very different perspectives when it comes to the research we consume. The things which you say are not cited absolutely should be, and if they are not that's a problem. In the papers I read, I'm often encountering citations to specific versions of libraries, specific industry created operating systems, industry created programming languages of which there are many, specific commercial lab equipment which were used. My friend in grad school used to do research on x86 machine instructions and he had a giant instruction manual on his desk at all times, which was cited thoroughly in his work. This is all part of doing good research.
Either way my point stands. Since they are citing the research as foundational in their papers, then we should take them at their word. The idea you put forth that they're only doing so as a matter of show puts a terrible light on them if true. They shouldn't be citing research as foundational if it really isn't. So I will choose not to believe your characterization, because I think very highly of the industry researchers I know, and that doesn't seem like something they would do.
First of all: we’re pretty far off topic now and I don’t think this particular point is at all relevant to the main thesis of this thread.
That said, even if we accept the general premise of your post, which I don’t, you’re still drawing the wrong conclusion.
To wit: citing something does not imply that the cited thing is “foundational” to the work from which it is cited. One can cite work for any number of reasons. (Admittedly, citation behavior did change with the rise of bibliomaniacs, but of course that further bolsters my overall point, so I’m not sure the daylight on this point does you any favors.)
You identified some counter-examples that miss the point because they’re unrepresentative, unresponsive, and irrelevant.
Unrepresentative because we are discussing literature in aggregate and this behavior is common.
Unresponsive because, in aggregate, inessential academic writing is systematically over-cited in academic writing and essential inputs of other types are systematically under-cited in academic writing. This is true of all academic writing; it’s a bias of the medium and of the medium’s standard bearers.
And irrelevant because there is nothing a priori or essentially nefarious about the above, on its own!
Academics beat ideas and lines of inquiry deep into the ground. Crucially, they do so by pumping out ridiculous quantities of PDFs. For every little variation there is a paper. Outside of academia this isn’t done. Eg: you cite Package X, great! But do you cite the 17 different PRs most relevant to your work, many of which are at least a papers worth or work? No. That’s culturally off. But for the corresponding thinly sliced papers that’s what you have to do.
Conclusion: academic work dominates the citation list because of publication and citation culture, not because academic work dominates the set of enabling contributions.
I do trust that you genuinely do experience the world as you describe here, but I think you’re a fish in water and that Upton Sinclair quote about paychecks comes to mind.
> citing something does not imply that the cited thing is “foundational” to the work from which it is cited.
I neither wrote nor implied that. Sure there are many reasons to cite papers, but in saying "citing the research as foundational", meaning that their foundation is the reason for their citation. You were so eager to write all those words you didn't stop to actually read mine. Therefore, I think that's all I have to say to you, I'll leave the rest unread.
Yes. My original post is about what people choose to cite, some small subset of which is ever cited as “foundational”. Why would you make this distinction then back track on it? Right: because it’s irrelevant point.
Pedantic and profoundly wrong but always in some ridiculous lens always wiggling enough to never let truth get on the way of Being Smart And Right. Peak .edu and the reason it’s so damn hard to justify science spending to the actually hard working tax payers patronizing this stuff.
This is an article about ML research, and the emphasis on branding and marketing your paper wouldn't fly in any of the fields people think of as scientific. Could you imagine someone saying, "be sure that the graphic for the molecule in figure 1 is 3D and has bright colors?"
The most disturbing thing about it is the way advice to forget about science and optimize for the process is mixed with standard tips for good communication. It shows that the community is so far gone that they don't see the difference.
If anyone needs a point of reference, just look at an algorithms and data structures journal to see what life is like with a typical rather than extreme level of problems.
Strongly disagree here. While I haven’t published e.g. particle physics work, I have authored/coauthored a number of peer-reviewed papers in other topics generally considered hard science (and published in ”high impact factor” scientific journals). This article and series is just as accurate about how ”Science 2” works outside of ML in my experiences. Branding and marketing is a very major factor in everything from grant funding to research publishing in academia.
There's a used car sales line somewhere and you just have to be careful to not cross it. Yes, there are rewards to good communication but if it becomes the sole purpose (communicating "read me") that's too far.
And it comes with rewards! The above lab is synonymous with several popular techniques (one, organocatalysis, which garnered a Nobel prize) - the association would be much less strong if the lab hadn't kept a consistent brand over so many years.
> This is an article about ML research, and the emphasis on branding and marketing your paper wouldn't fly in any of the fields people think of as scientific
The number of accepted papers is absolutely currency and measure of worth in academia.
Interestingly, one of the pieces of advice, about having a punchy title, is a double edged sword. There's some data suggesting papers with "clever" titles have an easier time getting published, but accumulate fewer lifetime citations.
As someone doing a literature review I can safely concur with this. Fancy titles often do not include the most obvious terms of what you are searching for, leading to fewer results from your query.
> The reason could be that clever titles add "novelty", but not much substance.
Another reason might be that clever titles stand out as bold claims, working counter to the common practice of academic humility. If a paper seems to be downplaying its own significance, then why should a casual reader (or reviewer, at first impression) give it the benefit of the doubt?
That's not to say that papers should over-claim, and I suspect that doing so might lead to a harsh counter-reaction from reviewers who feel like they've been set up to have their time wasted. Nonetheless, "project confidence" might be good practice in academia as well as one's social life.
That's true as a zeroth-order approximation, but even sticking to trivially quantified values your citation count is more important (that's maybe first-order), and on the level of your reputation the question you need to ask is, "will people feel like my work actually benefits them?"
It would be great if he’d shared the actual reviewer comments.
> Could you imagine someone saying, "be sure that the graphic for the molecule in figure 1 is 3D and has bright colors?"
I doubt the reviewers asked for that, but yes that kind of thing happens all the time prior to publishing and there’s nothing wrong with it. If it reduces the amount of time it takes to understand the paper then do it..
I have definitely said that people can't resist a network diagram, and "people love a good map", and I'm not in ML research. There are things that appeal to people.
This tends to not manifest as "We need one of these" but "If we have one of these, lets be sure to use it."
People like witty epigraphs underneath chapter titles too, and that's great. Now imagine saying, "the difference between this paper getting accepted or rejected is the presence or absence of a network diagram..."
I mean, at one point I was presenting some research next to someone with a network diagram, a map, and a phylogenetic tree and my comment was "That's going to win best poster" and I was right.
I think a lot of people are reading the presentation advice and thinking "yeah, I work hard for good presentation too," without realizing that the reason content hasn't even been mentioned is that the author really is describing ML accurately.
I think it's ultimately due to a lack of theory, which creates the expectation that the results from trying an idea will be a random draw. From that point, you get the behaviors of trying as much as possible and taking each attempt as a fixed object to then go try and get over the threshold.
I work in academia and have over 70 papers published.
I Agree with most ideas in the article.
Another dimension not covered is what I called “author engineering”.
Many times it is very difficult to “get into” a new field if you don’t have an author known by the editors. I work in applied math (very transversal) and happen to me often to be rejected because “I don’t belong to the area”.
PhD students usually don’t suffer from this as the supervisor is already a member of the community. But if not, try to bring a collaborator that is known in the area. This is usually done in conferences by chatting with people.
It never really worked otherwise. Even before formal peer review and journals, social standing and political squabbling, finding patrons etc. definitely affected science. The grade school textbook ideal version is a literal lie-to-children. The problem is that threading the needle correctly, without falling over to the other side, of quackery and "the academic mafia is suppressing this perpetual motion machine" and "Big Science" doesn't want to admit that ESP exists etc. can be incredibly hard.
It's a pet peeve of mine, but "publications" and "peer-review" are not really part of the scientific process? Just like "academia" they have kind of grown onto the term, almost claiming it for their own. I find that a sad evolution.
The most fun in science can be had when done at home and shared with friends.
Academia != science. It is a social construct and dominated by people with power within a given field. That being said, double blind review process improved the author engineering problem a lot.
The author elsewhere cautions “I write this because PhDs seem to attract a lot of smart, idealistic kids who are interested in doing Science 1 and don’t realize that they’ve signed up to do Science 2.”
But in practice it absolutely is how the academic world works. Is politics all the way down.
Which is why it's so funny when you see non skeptical appeals to "the god of science" which apparently exists in a vacuum of correctness and ethical purity.
And there's more where that came from. This particular sausage is made with tragically messed up incentives, and people will naturally always optimize the framework you put them in.
Thankfully the scientific process is incredibly resilient to nonsense, because a bad result will eventually screw up someone's future work when they come to rely on it. But it's not pretty.
> because a bad result will eventually screw up someone's future
Not if they isolate themselves enough from the outcome but I get what you're saying.
The world progresses despite these deeply flawed institutions (corporations or academia have these perverse incentive problems and all in all, they do create some value on average).
> Many times it is very difficult to “get into” a new field if you don’t have an author known by the editors.
Although there's plenty of critique to go around about the review system, machine learning here typically uses double-blind peer review for the major conferences. That blinding is often imperfect (e.g. if a paper very obviously uses a dataset or cluster proprietary to a major company), but it's not precise enough to reject a paper based on the author being an unknown.
Yes, with some caveats. Sometimes people can guess where a paper comes from based on the used datasets, even graphic design style, a skew in cited papers. Also, often people upload the preprint to Arxiv and so the reviewer may have seen the non-anonymous version already. Also, the having someone within the community among the authors will help with formulating the paper in a way that this community expects it. With the right turns of phrase, citing and praising those related works that are seen highly in the community, using the tone that is usual in the community etc. People should ideally try to counteract such biases in themselves, but humans are tribal and social. Especially if you scale this to tens of thousands of people, the average won't be a saint. People have careers, graduations, promotions, visas, green cards, job prospects, friendships and generally social standing in their professional community on the line. Academics aren't any more holy than people in finance, or politics or entertainment or startups or other ruthless and social-game heavy environments.
My wife works in a fairly niche field and can often guess at the very least which university or research group a blind paper is from, and quite often the author (or in the case of a PhD student, the authors supervisor).
There’s tons of extra work in double blind (for the author mostly, but also for peer reviewers and editors/chairs). Speaking from direct experience as author, reviewer, and chair at conferences. And generally the benefit is totally lost as you can easily circumvent it especially if you have already published in the field by just citing your previous work.
> And it’s not just a pace thing, there’s a threshold of clarity that divides learned nothing from got at least one new idea.
But these days, ideas are quite cheap: in my experience, most researchers have more ideas than students to work on them. Many papers can fit their "core idea" in a tweet or two, and in many cases someone has already tweeted the idea in one form or another. Some ideas are better than others, but there's a lot of "reasonable" ideas out there.
Any of these ideas can be a paper, but what makes it science can't just be the fact that it was communicated clearly. It wouldn't be science unless you perform experiments (that accurately implement the "idea") and faithfully report the results. (Reviewers may add an additional constraint: that the results must look "good".)
So what does science have to do with reviewers' fixation on clarity and presentation? I claim: absolutely nothing. You can pretty much say whatever you want as long as it sounds reasonable and is communicated clearly (and of course the results look good). Even if the over-worked PhD student screws up the evaluation script a bit and the results are in their favor (oops!), the reviewers are not going to notice so long as the ideas are presented clearly.
Clear communication is important, but science cannot just be communicating ideas.
Clarity is and should be absolutely crucial, though.
As an academic I need to be up to date in my discipline, which means skimming hundreds of titles, dozens of abstracts and papers, and thoroughly reading several papers a week, in the context of a job that needs many other things done.
Papers that require 5x the time to read because they're unnecessarily unclear and I need to jump around deciphering what the authors mean are wasting me and many others' time (as are those with misleading titles or abstracts), and probably won't be read unless absolutely needed. They are better caught at the peer review stage. And lack of clarity can also often cause lack of reproducibility when some minor but necessary detail is left ambiguous.
Clarity is relative. You can be super clear, but if it goes against what the reviewer thinks they know, it will be perceived as unclear. You can also point to references that clear up any remaining doubt about how something is meant, but of course the reviewer will never check out these references.
In the end, getting a paper accepted is a purely social game, and has not much to do with how clear your science is described, especially for truly novel research.
1) The whole opening segment is the literature review.
2) If you are coming up with a novel concept, then you would be explaining how it shows up in relation to known fact.
Then you would be providing evidence and experiment.
The entire structure is designed to ensure as many affordances for the author to make their case.
Being accepted as a social game is the cynical view that ignores that academia still works. It’s academia itself which recognizes these issues and is trying to rectify the situation.
As you have seen in the post (did you read it, dear reviewer?), references on the first page where unhelpful, so 1) comes already with a caveat.
And so on.
I think the social game view is at this point entirely justified, and there is nothing cynical about it. And no, academia does not still work.
A given "structure" is also ridiculous, and part of the problem. Once you care more about the form than the content, form is what prevails.
The truth is: To understand a paper properly, you need to deal with it properly, not just the 5 minutes it takes to skim the first pages and make up your opinion there already. Fifteen pages is short enough, and if you cannot commit to properly review this, for a week or so of dedicated study, just don't review it. We would all be better off for it.
> The truth is: To understand a paper properly, you need to deal with it properly, not just the 5 minutes it takes to skim the first pages and make up your opinion there already. Fifteen pages is short enough, and if you cannot commit to properly review this, for a week or so of dedicated study, just don't review it. We would all be better off for it.
Reviewing dynamics make this hard. There is little to no reward for reviewers, and it is much easier to write a long and bad paper than it is to review it carefully (and LLMs have upset this balance even further). To suggest that every submitted paper should occupy several weeks of expert attention is to fundamentally misunderstand how many crappy papers are getting submitted.
> But these days, ideas are quite cheap: in my experience, most researchers have more ideas than students to work on them.
By “idea” researchers usually imply “idea for a high-impact project that I’m capable of executing”. It’s not just about having ideas, but about having ideas that will actually make an impact on your field. Those again come in two flavors: “obvious ideas” that are the logical next step in a chain of incremental improvements, but that no one yet had time or capability to implement; and “surprising ideas” that can really turn a research field upside down if it works, but is inherently a high-risk/high-reward scenario.
Speaking as a physicist, I find the truly “surprising ideas” to be quite rare but important. I get them from time to time but it can take years between. But the “obvious” ideas, sure, the more students I have the more of them I’d work on.
> Any of these ideas can be a paper, but what makes it science can't just be the fact that it was communicated clearly. It wouldn't be science unless you perform experiments (that accurately implement the "idea") and faithfully report the results. (Reviewers may add an additional constraint: that the results must look "good".)
I kinda agree with this. With the caveat that I’d consider e.g. solving theoretical problems to also count under “experiment” in this specific sentence, since science is arguably not just about gathering data but also developing a coherent understanding of it. Which is why theoretical and numerical physics count as “science”.
On the other hand, I think textbooks and review papers are crucial for science as a social process. We often have to try to consolidate the knowledge gathered from different research directions before we can move forward. That part is about clear communication more than new research.
It's not too difficult to state any idea, even a surprising one. But often, papers with surprising ideas (or maybe the right thing to say is surprising results?) turn out to be wrong!
I think it's still the case that there's lots of ideas that (if they worked!) would be surprising. Anyone can state outlandish ideas in a paper -- imo the contribution is proving (e.g. with sound "experiments", interpreted broadly) that they actually work. Unfortunately, I think clarity of writing matters more to reviewers than the soundness of your experiments. I think in CS this could very well change if the reviewers willed it (i.e. require artifact submission with the paper, and allow papers to be rejected for faults in the artifact)
I think in some ways science has been co-opted by careerists who try to minmax output to accelerate their careers. Being idea obsessed is part of this. It’s much easier to get a paper published that’s on the hype train as opposed to a paper that challenges some idea. Publications justify grant money, grant money justifies more people and more power, more power justifies promotions. And if you talk with early career scientists they all will say they are only doing it until they get a permanent position. Then they will become more curious. Maybe they do, maybe they don’t, I have many older colleagues who are quite curious compared to their younger counterparts. but I believe rewarding ambition at the expense of curiosity is somewhat anti intellectual. It’s sad because I think science should reorganise as the current structure of departments into disciplines may be dated and restructuring could help alleviate this a lot since interdisciplinary work may leverage curiousity over ambition as curiosity will be rewarded with high impact work. But who knows. I can arm chair my way into anything.
The point of a paper isn't "I had this idea" nor is it "I have this evidence". It is "I had this idea, and it turned out to work! (btw here's the evidence I found that convinced me it works).
The value lies in getting true ideas in front of your eyeballs. So communicating the idea clearly is crucial to making the value available.
I agree with you. But what part of the blog post, or the peer review process in general, do you think ensures that only true ideas get in front of eyeballs?
I can write anything I want in the paper, but at the end of the day my experiments could do something slightly (or completely) different. Where are reviewers going to catch this?
This person is a clown, probably with a paid agenda, and they should be disallowed from saying such dumb things where smart people with useful skills might read it.
I have a theory that this focus on ideas vs solutions also divides individual researchers, in what drives them. Agreed that academia celebrates and rewards ideas, not solutions. And maybe that’s ok and how it should be, solutions can be done in industry? But the SNR of ideas feels too high at this point.
generating the ideas “planets move at constant per planet velocity” “planets move at a specific speed as a power law function of distance from the sun and we fit the paramets great” “each planet sweeps equal areas in equal time” is cheap, but evaluating which idea is good is expensive, and the whole value of that evaluation is captured in the final idea
This whole take is embarrassingly ignorant and no one with the credentials has the time to check you. We need people to do real thinking and they need to ignore you.
I don't understand why took the time to leave (three?) personal attacks, rather than just provide your perspective? I'm willing to acknowledge that my opinion has limitations (I think it mainly applies to CS-adjacent fields with empirical performance evaluations, and where experiments can easily be independently verified).
Thanks for this post. As someone writing an open-source book (without an editor to help), I find some takeaways very helpful.
But I think your most significant change was changing the "what" to "why".
Reading the original, we can see that most sentences start with "we did..." "we did..." and my impression as a reader was, "Okay, but how is this important?" In the second one, the "what" is only in the first part of the sentence, to name things (which gives a sense of novelty), and then only "whys" come after it.
"Whys" > "Whats" also applies to good code comments (and why LLM's code sometimes sucks). I can easily know "what" the code does, but often, I want to know "why" it is there.
I am not the original author, but I posted this since it mirrors some experiences I have had in my PhD so far submitting papers. This kind of tweaking in paper and writing even happens when writing the first draft or sometimes even in the conception of the research idea or how to go about the implementation and experimentation.
There is a half-joke in our lab that the more times a paper is rejected, the bigger or more praised it will be once it's accepted. This simply alludes to the fact that many times reviewers can be bothered with seeing value in certain ideas or topics in a field unless it is "novel" or the paper is written in a way that is geared towards them, rather than being relegated to "just engineering effort" (this is my biased experience). However, tailoring and submitting certain ideas/papers to venues that value the specific work is the best way I have found to work around this (but even then it takes some time to really understand which conferences value which style of work, even if it appears they value it).
I do think there is some saving grace in the section the author writes about "The Science Thing Was Improved," implying that these changes in the paper make the paper better and easier to read. I do agree very much with this; many times, people have bad figures, poor tables or charts, bad captions, etc., that make things harder to understand or outright misleading. But I only agree with the author to a certain extent. Rather, I think that there should also be changes made on the other side, the side of the reviewer or venue, to provide high-quality reviews and assessments of papers. But I think this is a bit outside the scope of what the author talks about in their post.
There are other posts in the series of the author. He was the co-author of BERT! Yet his paper was scoffed at as "just engineering". He knows what he is talking about.
Omg, I was not a BERT coauthor! But thank you so much for writing this, I had no idea that other post could have accidentally implied this. I will revise that section.
I have a running joke with my friends. "If your paper is not rejected once, what kind of science are you doing". Either you spent too much time on a project or aiming low.
I think it’s equally likely that the second version just got a different set of reviewers who randomly liked it more, and the revisions didn’t make a big difference. Having submitted lots of papers to conferences like this I basically think of the reviewer ratings as noise.
For both grants and papers in my experience, there's a "Doomed"/"Not Doomed" threshold you have to get over, but if you clear that threshold things get fairly stochastic.
For *ACL you'd have to justify your wish to change reviewers, though; and you need a good reason for that. I don't know how much reviewers changes for a resubmission are solely due to reviewers' unavailability but it seems unlikely all three of them got removed from the reviewer pool.
I think it would benefit people to look a few layers above themselves, and to see the big picture of the system, who the different actors are, what their goals are, how they are pursuing them etc. Like the "follow the money" game where juniors in corporations are told to try to understand the flow of money and business value and revenue as soon as possible, in order to know how to advance their corporate careers.
In academia the equivalent is prestige. Who gets it and how? Who are the players? There are college students, PhD students, professors, administrators, grant committees, corporation-university industrial collaborations and consortiums, individual managers at corporations and their shareholders, university boards, funding agency managers, politicians allocating taxpayer money to research funding, journal editors, reviewers, tenure committees, pop science magazine editors, pop science magazine readers, general public taxpayers.
You should be able to put yourself in the shoes of each of these and have a rough idea of how they can obtain prestige as input from some other actor and how they can pass on prestige to yet another actor. You must understand the flow of prestige, and then it will be much less mysterious. (Of course understanding the flow of money also helps, but people tend to overlook prestige because one of the least prestigious things is to overtly care about prestige, it's supposed to seem effortless and unacknowledged)
I was surprised to see the author claim that citations in the Introduction are a bad thing. I do think ML papers are generally pretty bad at acknowledging other relevant literature, but this makes me think it’s an active decision somehow
Looking at the differences between the rejected and accepted papers, I don't think it's quite a matter of 'avoiding citations'. The changes seem to break along two lines.
1. Avoid overly general citations. The rejected paper leads with references to image captioning tasks in general and visual question-answering, neither of which is directly advanced by the described study. The accepted paper avoids these general citations in favour of more specific literature that works directly on the image-comparison task.
2. Don't lead with citations. The accepted paper has its citations at the end of the introduction, on page 2.
I think that each change is reasonably justified.
In avoiding overly-general citations, the common practice in machine learning literature is to publish short papers (10 pages or fewer for the main body), and column inches spent in an exhaustive literature review are inches not spent clearly describing the new study.
Placing citations towards the end of the introduction is consistent with the "inverted pyramid" school of writing, most commonly seen in journalism. Leaving the review process out of it for the moment, an ordinary researcher reading the article probably would rather know what the paper is claiming more than what the paper is citing. A page-one that can tell a reader whether they'll be interested in the rest of the article does readers a service.
You should definitely cite, but move the citations to where they are the most relevant and make them specific. The abstract and introduction should be more focused on what you've achieved, and overview of how you've achieved it, and why it is interesting. There generally shouldn't be too much to cite here. The exact details of methods used and everything you've built on comes later in the paper and that is where citations become important and relevant.
My least favourite type of citations in introductions, that I often see from more junior researches are ones that look like:
"In this paper we use a Machine Learning [1][2][3] technique known as Convolutional [4] Neural Networks [5][6][7][8] to..."
Ot was a fun read, but, how do you know these changes made your paper better? Your assumption is that reviewers approach the reviewing process with the same knowledge and goals, or are quite objective, but that's mot the case in all my publication history. So how can you prove causal effects with 1 sample?
Another strategy is to write a descent paper and submit it somewhere good. If it’s accepted, great. If it’s rejected and the comments make sense, improve the paper based on the comments before resubmitting somewhere else. Otherwise simply resubmit somewhere else.
Another Machiavellian thing I have seen in the literature related to "Science 2" is where ML benchmarks or test cases only become accepted when they show that a lot of people's models are working. ;-)
There is saying in english that people "eat with their eyes".
When it comes to papers, I always reminded myself and others that people also _read_ with their eyes.
It is easy to be cynical about this (with some justification!), but if the findings are more clearly and quickly communicated by a pretty-looking paper, then the paper has objectively improved.
It seems to go 180 degrees against what a smart starry-eyed junior grad student would believe. Surely, it's all about actually making things work, right? We are in the hard sciences, we don't just craft narratives about our ideas, we make cold hard useful things that are objectively and measurably better and can be used by others, building on top of it, standing on our shoulders, and what could be more satisfying than seeing the fruits of our research being applied and used.
However, for an academic career you want to cultivate the profile of a guru, a thought leader, a visionary, a grand ideas person. Fiddling with the details to put a working system together is lowly and kinda dirty work, like fixing clogged toilets or something. Not like the glorious intellectual work of thinking up great noble thoughts about the big picture.
If you want to pivot to industry, it could help you to build a track record of having created working systems, sure. But I've often seen grad students get stuck on developing bepoke internal systems that are not even really visible to potential future employers. Like improving the internal compute cluster tooling, automating the generations of figures in Latex, building a course management system to keep track of assignment submissions and exam grading and so on. Especially when you're at a phase where your research project is getting rejections and you feel stuck, you are most prone to dive into these invisible, career-killing types of work. In academia, what counts is your published research, your networking opportunities obtained through going to conferences where you have papers, getting cold emailed because someone saw your paper etc. I've seen very smart PhD students get stuck in engineering rabbit holes and it's sad. It happens less if your parents were already in academia, and you kinda get the gist of how things work via osmosis. But outsiders don't really grok what actually makes a difference and what is totally invisible (and a waste from a career perspective). Another such trap is pouring insane amounts of hours into teaching assistance and improving the materials, slides, handouts and so on. The careerists will know to spend just as much on this sort of stuff as they absolutely have to. Satisficing, not optimizing. Do enough to meet the bar, and not one minute more. It is absolutely invisible to the wider academic research community whether your tutorial session on Tuesday to those 20 students was stellar or just OK. Winners of the metagame ruthlessly optimize for visible impact and offload everything else to someone else or just not do them. A publication is visible. A research semester at a prestigious university is visible. Getting a grant is visible. Being the organizer of a workshop is visible. Meticulously grading written exams is invisible. Giving a good tutorial session is invisible. Improving the compute infrastructure of the lab is invisible. Being the goto person regarding Linux issues is invisible.
Packaging your research in a way that works well out of the box is in the middle on this spectrum. It may be appreciated by another stressed PhD student somewhere in some other university, and it may save them some time in setting things up. But that other PhD student won't sit on your grant committee or promotion board. So it might as well be invisible. Unless your work is so stellar and above and beyond other things that it goes viral and you become known to the community through it. But it's a double edged sword, because being known for having packaged your work in an easy to use manner will get you pigeonholed into the "software engineer technician" category, and not the "ideas person" category. Execution is useful but not prestigious. Like the loser classmate whose homework gets copied but isn't invited to parties.
The metagame winner recognizes that their work is transient. Any time spent on packaging up the research software for ease of use or ease of reproducibility once the publication is accepted is simply time stolen from the next project that could get you another publication. Since you'll likely improve the performance in the next slice of the salami anyway, there would be no use in releasing that outdated software so nicely. The primary research output is the paper itself, and the talks and posts you can make to market it to boost its citations, as well as the networking opportunities that happen around the poster and the conference. Extras beyond that are nice, but optional.
While you're working on making something "really" work, you're either delaying the publication, making it risky to get scooped (if done before publication), or you're dumping time into a dead project (dead in the sense that the paper is already published and won't be published-er by pouring more time into it post-publication).
Sure, but many people who go for a PhD aren't the entrepreneur type. And in a corporation there are also "games", where building things that actually work may not be your best strategy. You need to work on visible, flashy, new things that looks good in a performance review and can get you a promotion. You have to deliver legible value.
Yes, that’s all correct. There’s always a meta-game.
Part of the meta-game of academia is that feedback timelines are long enough that you can play the “wrong” meta-game and still come out ahead. If you don’t want a professorship — or are willing to settle for a super cushy “professor of practice” as an early retirement non-profit thing to keep ya out of the house — then a PhD can be a good place to do hard tech pre-seed work.
I was reading this and thinking that the research could be used by LLMs to identify birds using the Birds-to-Words dataset identified in this research paper.
Interesting tips, but it also depends on the field.
If you're submitting to a control theory journal, you better have some novel theorems with rigorous mathematical proofs in that "rest of the paper" part. That's a little nontrivial.
Sure, but if you can't articulate why those theorems and proofs are important to be pursued, how it's different from all the related works, what your unique contribution is and why that matters, what the previous works lacked or got wrong, what the impact and value of your work is, i.e. why anyone should care at all, then you'll have a hard time getting it accepted. Just because the math checks out and was difficult and took a lot of effort, it doesn't guarantee that the work is worthy of dissemination.
I have a problem with this. In the old days, people did research for the sake of research, and mostly out of Europe came the greatest scientific works we have seen. I did my PhD in the US, and it is very unfortunate that "gaming" publications and focusing on "grants" is the meat of research. Before I get criticized, I was part of this process at a top 10 university and I am a proud American. It is because of this pride that I must show tough love. I chose to move away from academia without a postdoc because I hated it. I wanted to do research and contribute to work that pushes my field forward. Most (90% of those I met, and I dare say 99% of international students) only wanted a PhD for selfish reasons (entry to US market, salary bump, changing fields, access to RnD jobs, etc). Perhaps I am naive, but I wish more people did research for the sake of research. The only Clay prize went to a Russian who hated academia. Perhaps there is some truth in the fact the immortals in science are not those churning conference papers, but those laying seeds a la Laplace, Einstein, etc. I want to see more of those, because this is what will move the field forward. It is not manipulating metrics to improve a neural network for one use case, while knowing (and not sharing) it fails in every other instance. This is my second beef with research. When something is tried but does not work, it is not shared. Someone else will try and fail, and this build up will overall slow everyone down. I wish we were more accepting of failed trials, and of not knowing the answer (sharing results without the theory is OKAY. It is OKAY if someone else comes up with it using your results. Having spent many years in a PhD, I can confirm the vast majority unfortunately do not share my point of view. And I hope I do not come across as bitter, it frankly makes me sad.
"In the old days, people did research for the sake of research, and mostly out of Europe came the greatest scientific works we have seen."
In the old days, scientific careers were largely restricted to the independently wealthy or those who could secure patrons.
I also feel like there's a sort of tension with what Hacker News broadly wants out of science. There's often a lament that there aren't enough staff science positions, or positions where people can have a career beyond a postdoc that's just devoted to research.
Those things have to be paid for. Postdocs are expensive. Staff scientists are expensive - and terrifying, because they have careers and kids and mortgages. Postdocs are expensive.
That ends up eating a lot of a PIs time, because the success rate on proposals are low. Even worse now.
Would I love to be able to just sit in my office, think my thoughts, and occasionally write those thoughts up? Sure. But I'd also like to give people an opportunity to have careers in science where they can get paid.
The Idea of a staff Scientist is to Help with writing proposals, teaching and doing Research. It's also the only way of conserving the tacit knowledge of the Research group. Your PhD Student and Postdocs are gone after 3-6 years, and often enough the knowledge generated in this time is leaving with them. You are Not sitting around and writing Up your thoughts.
Staff Scientists don't help with teaching, essentially by definition, unless we dilute teaching to the very broad level of "helping students with things". They are certainly helpful for research, and in my experience only somewhat useful for writing proposals - certainly not to the point that they'd be self-funding (rare is the staff scientist who is good at writing proposals, wants to, yet does not want to be a PI).
None of that gets to the actual point of my comment, which is that it's all well and good to say people should do science for science's sake, but in the meantime, rent is due.
I think a more charitable reading is that these are just basic suggestions about how to make one's writing clear and get your point across. It's hard to step back and look at what you write from the perspective of someone not familiar with the subject matter (ie. the reviewer).
Sure it's framed in terms of "helping you get published" (which feels kind of gross) but I think ultimately it's really about tips for authors to get their points across in a clear and engaging way.
I mean, at some point science is communication. Great for Einstein if he gets general relativity, but if he wants anyone else to care, he needs to communicate not only the complex idea in a clear manner, but also _why_ I should spend my cherished minutes here on earth trying to wrap my small brain around it.
It's the difference between being a Cassandra or the Oracle at Delphi. Maybe the only difference between the two was presentation? (Classicists, feel free to roast my metaphor).
The argument is about pursuing research for discovery or pursuing research for career advancement. Both scenarios require communication but for different reasons. You're not really addressing main critique.
> I did my PhD in the US, and it is very unfortunate that "gaming" publications and focusing on "grants" is the meat of research.
I interpreted GPs above statement as reacting to the strategies in the article for "gaming" publications and grants, but perhaps I misunderstood GPs comment.
My argument is time vested into this is time away from discovery and research. Communication is certainly a valuable skills. But by most accounts, significant effort is spent on grants and to make publications appealing to employers (the author himself argues for fun branding in a scientific paper). Instead, I argue the focus should be on advancing the field. Of course, you can argue that conference papers with fun branding and a neural network improvement in one benchmark (where selection bias makes the model look robust), is advancing the field. And it's all about getting paper accepted, so I can get a high h-index, so I can get a high paying job or a job in academia. But I believe the real impact of scientific research is in its footprint on the next generation, and I sincerely doubt any of these papers (using fun branding and focusing on the wrong things), will have an impact then. I hope I am proven wrong.
The two are not as orthogonal as the cynical takes make it seem. This is also a bit like the idea that attractive people are less smart and vice versa, when the two are actually positively correlated.
The people who won the career game at top US universities in technical fields don't simply get there by making their plots fancier or using the right words in the abstract in otherwise trivial papers. The papers do make valuable contributions. Pursuing research for pure personal discovery is great, but if you don't tell others about it, why should they care? Most discoveries are not General Relativity or Evolution.
And there's also a component of "cope" in these lamentations. Oh, I'm a lone wolf genius, misunderstood by all, the contrarian who is rejected by the in-crowd yadda yadda because of career failure. It's a way to preserve ego. If only it wasn't for the social games, I'd be the next Einstein, my intentions are pure, while the establishment is rotten. It's a bit more nuanced than that. You have to do good work AND know how to present it and spread awareness about it. Both are needed.
You're not really addressing the point head on. The argument about how science is communicated is orthogonal. The argument is that science academia prioritizes career over pursuing academic science.
I won't speak for anyone else but here are three things I think are all true:
* We live in a renaissance of academic research that is giving us profound scientific discovery
* Prioritizing a scientific career over scientific discovery can lead to a net positive of good scientific results, and, so far, has
* Prioritizing a scientific career over scientific discovery produces low quality science
Saying that people who know how to maneuver the political academic landscape, to secure a position, also produce valuable contributions might be true (I believe it is) but the argument doesn't address the cost of prioritizing, or promoting, that behavior.
I'm reminded of "The Economics of Superstars" [0]. If someone is "better" by a measure of 2x, say, but gets (10+)x the amount of resources, this is not a good allocation of energy. Saying that the 2x person should get more resources is true. Saying that they're justified in getting orders of magnitude more resources, at the cost of everyone else who might use them to better effect, is not.
These conversations are subtle. I notice that one of the common crutches is to attack people as "just being bitter". This seems like a cheap attack and I wish you and others would try to be more thoughtful.
I'm a bit afraid that some people will read this article or skim it and say "The fact that you have to do all of this 'branding' is just further proof that science is riddled with irredeemable incentive issues." However, this isn't the author's point. In fact, early the the post, the author writes:
>The tweaks that get the paper accepted—unexpectedly, happily—also improve the actual science contribution.
>The main point is that your paper’s value should be obvious, not that is must be enormous.
This is slightly oversimplified, but from the outside, science may look like researchers are constantly publishing papers sort of for the sake of it. However, the papers are the codified ways in which we attempt to influence the thinking of other researchers. All of us who engage in scientific research aim to be on the literal cutting edge of the research conversation. Therefore it's imperative to communicate how our work can be valuable to specific readers.
Let's take a look at the two abstracts:
(Version 1, Rejected): Given two distinct stimuli, humans can compare and contrast them using natural language. The comparative language that arises is grounded in structural commonalities of the subjects. We study the task of generating comparative language in a visual setting, where two images provide the context for the description. This setting offers a new approach for aiding humans in fine grained recognition, where a model explains the semantics of a visual space by describing the difference between two stimuli. We collect a dataset of paragraphs comparing pairs of bird photographs, proposing a sampling algorithm that leverages both taxonomic and visual metrics of similarity. We present a novel model architecture for generating comparative language given two images as input, and validate its performance both on automatic metrics and visa human comprehension.
Here, the first two sentences a) make a really obvious claim and could equally be at home in a philosophy journal, a linguistic journal, a cognitive science journal, a psychology journal, a neuroscience journal, even something about optometry. Moreover, some readers may look at this abstract and think "well, that's nice, but I'm not sure I need to read this."
(Version 2, Accepted): We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., “heart-shaped face,” “squat body”). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance—drawn from a novel stratified sampling approach—with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.
Compared to V1, the V2 abstract does a much better job of communicating a) how this project might be valuable to people who want to understand and use neural-network models "to explain differences in visual embedding space using natural language." Or to put it another way, if you want to understand this, it's in your interest to read the paper!
Oh dear... a monkey has escaped from the circus and is telling us the truth about what's going on inside it.
> "The primary objects of modern science are research papers. Research papers are acts of communication. Few people will actually download and use our dataset. Nobody will download and use our model—they can’t, it’s locked inside Google’s proprietary stack."
The author is confusing the concept of 'science as a pursuit that will earn me enough money and prestige to live a nice life' - in which, I'd say, we can replace 'science' with 'religion' and go back to the 1300s or so - with science as the practice of observation, experiment and mathematical theory with the goal of gaining some understanding of the marvelously wonderful universe we exist in.
Yes, the academic system has been grotesquely corrupted by Bayh-Dole, yes, the academic system is internal blood sport politics for a limited number of posts, yes, it's all collapsing under the weight of corporate corruption and a degenerate ruling class - but so what, science doesn't care. It can all go dormant for 100 years, it has before, hasn't it? 125 years ago you had to learn to read German to be up on modern scientific developments.
Wake up - nature doesn't care about the academic system, and science isn't reliant on some decrepit corrupt priesthood.
P.S. Practically speaking, new graduate students should all be required to read Machiavelli as an intro to their new life.
From the first article in the series [0]:
> Insiders ... understand that a research paper serves ... in increasing importance ... Currency, An advertisement, Brand marketing ... in contrast to what outsiders .. believe, which is ... to share a novel discovery with the world in a detailed report.
I can believe it's absolutely true. And yikes.
Other than the brutal contempt, TFA looks like pretty good advice.
[0] https://maxwellforbes.com/posts/your-paper-is-an-ad/
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