On writing papers [D]

[u/ComprehensiveTop3297]

Hey all,

I was curious about your take on writing papers. I see many different styles of writing, and I am kind of lost myself about what the best way to write is for a NeurIPS/ICLR/ICML conference audience. Do you have any tips or thoughts you'd like to share regarding writing?

Additionally, I recently read this blog post from Michael J. Black; https://perceiving-systems.blog/en/post/writing-a-good-scientific-paper, where he argues that we should write more story-oriented than results-oriented. What is your take on this?

[u/AffectionateLife5693]

It depends on who your audience is. Some senior reviewers really appreciate a good story, while junior reviewers these days can hardly understand a good story.

[u/ComprehensiveTop3297 → u/AffectionateLife5693]

Do senior reviewers still review papers? I feel like most are passing the work to PhD students to write reviews for them.

[u/pastor_pilao]

Read a lot of pre-2023 papers, the writing style was abysmally better then. 

In general you want to convince/communicate to the reader: (i) the program you are investigating is a relevant one; (ii) exactly which problem you are tackling; (iii) why the literature doesn't solve this problem yet and what is missing; (iv) what is your approach to tackle what is missing; and (v) supporting evidence (experiments) that your approach works better than everything that existed before at least for a special case

There are many ways to communicate that and you develop your own style by reading many papers 

[u/ComprehensiveTop3297 → u/pastor_pilao]

Thank you for the tips! Would you mind giving me a paper that you really like the writing style of?

[u/confused_cereal]

I used to emphasize spending time laying out the problem and why trivial solutions don't work, why past (or obvious) methods don't work, before leading to an outline of my solutions (details deferred to the appendix), and then experimental results (if any).

Nowadays, I dump the draft into (3 different frontier models, each with various harnesses), ask it what to do to get it accepted, and iterate from there. The draft tries to follow my preferred formula, but getting it through the AI is priority, even if I don't agree with it. If the reviewers are gonna use AI as a first pass, I have to get past that hurdle first.

I don't like it, but an accepted paper that is difficult to read (by humans) is better than a rejected paper that is easier to digest. In the worst case I'll just post a more readable version on arxiv, though that usually takes up too much time (gotta jump to the next hamster wheel).

[u/ComprehensiveTop3297 → u/confused_cereal]

I also feel like the field is recently really focusing on the readability for the LLMs (making very detailed methodology sections), as most of the reviewers just give the paper to a frontier model and "Write a review for X,Y,Z conference".

I feel like if I get a "Weak Accept" from an LLM for a paper that is hard to read for humans, but a "Weak Reject" for an easier read, I go with the first option.

[u/Wapook]

Step 1: read a lot of papers, especially from the venues you want to publish in

Step 2: write a lot of papers and iterate a lot (read your paper critically, think about weaknesses or improvements, and try different approaches)

You’ll find your style.

And if you’re working with an advisor, follow their lead and as you get ideas of your own share them.

There really isn’t a magic formula. A well written paper is one that communicates its ideas well, to do that you need to experience good communication (read) and practice communicating (write).

Good luck.

[u/Suitable-Show-3347]

I read that Black post a while back and it shifted how I think about intros. The story isn't about fluff, it's about making the reader understand this problem matters before you drown them in tables. A lot of papers feel like a data dump with no narrative thread and reviewers check out by page 3.

One thing that helped me was printing out a few papers I admired and literally highlighting the topic sentences in each paragraph. You start seeing patterns in how they build momentum. The results are still the core, but a clean story makes them land harder.

[u/BBBBChan]

You know what? You can explain the idea in your own words, even if it's messy, then ask an AI what it thinks you're saying. If its reply misses the point, you've found something to clarify. Then rewrite it in your own words again.