hckrnws
Ask HN: Any nerds out there who've read a lot of research papers?
by shriy-singh
by shriy-singh
Smith, Gordon C. S., and Jill P. Pell. 2003. “Parachute Use to Prevent Death and Major Trauma Related to Gravitational Challenge: Systematic Review of Randomised Controlled Trials.” BMJ 327 (7429): 1459–61. https://doi.org/10.1136/bmj.327.7429.1459.
Objectives: To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.
Design: Systematic review of randomised controlled trials.
Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.
Study selection: Studies showing the effects of using a parachute during free fall.
Main outcome measure: Death or major trauma, defined as an injury severity score > 15.
Results: We were unable to identify any randomised controlled trials of parachute intervention.
Conclusions: As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.
Some titles stick. "Attention is all you need". "SSA is functional programming". Plus more people see the title than read the abstract. Only one I got to name myself was "Shared Memory Remote Procedure Calls" which I now realise is in exactly the same theme of given the title, you don't need the abstract or the contents.
Different fields use different standard forms or styles for abstracts. If you have not already done so you can get a feel for this by looking at a random sample of the top 100 most-cited papers in your field (or your target conferences, journals, arxiv categories) and quickly compare to top 100 in a couple of disparate fields (e.g. biochemistry, economics, anthropology, mathematics).
Of course, there are good reasons to read a paper besides the quality and content of the abstract. Consequently, there are well known papers with non-standard abstracts. I recommend mastering the basics first.
IMO the most memorable papers contain some unexpected simplification: a complicated problem turns out to reduce to something much simpler [0], perhaps for a counterintuitive reason [1].
So a memorable abstract should advertise that the paper contains some cute little trick. This may not help you actually publish though
[0]: Attack the RLHF problem with a simple classification loss: https://arxiv.org/abs/2305.18290
[1]: Frustratingly Easy Meta-Embeddings: https://arxiv.org/abs/1804.05262
There's also the infamous opening paragraph to the "States of Matter" textbook by David L. Goodstein:
Ludwig Boltzmann, who spent much of his life studying statistical mechanics, died in 1906, by his own hand. Paul Ehrenfest, carrying on the work, died similarly in 1933. Now it is our turn to study statistical mechanics.
I don't have any examples to offer; but you may be missing your calling in Marketing
include a SUMMARIUM in Latin and ΣΥΝΟΨΙΣ in Greek, for that real Scholar effect.
Short, succinct and no AI in sight.
"Abstract The Paxos algorithm, when presented in plain English, is very simple."
For example, I quite enjoyed the paper “Web Browser Fingerprinting Using Only Cascading Style Sheets,” but its abstract isn’t anything overly exciting. The title itself conveyed enough information for me to gather my interest, its attract just confirmed it was probably worth reading it.
- how do you actually go about reading one of these IEEE or arxiv papers?
- do you read only the abstract for most?
- do you read everything in 1 scan and what do you about references? do you follow through all the references?
- do you ask a GPT to help explain? what does your prompt look like?
1. First read the abstract and conclusion
2. Then flip through the figures and their captions, and try to understand them as best as you can (though this doesn't work for all papers)
3. Make a deliberate decision about whether it's worth it to dive into the details and read the full text.
LLMs are incredibly useful for parsing dense literature, but I think it's better to ask specific questions about the paper, jargon, equations etc. rather than to have it summarize the paper for you. Summaries are often shallow or even misleading, and they rob you of the opportunity to engage actively with the paper.
+1 point for honesty.
-100 for being pathetically pathetic
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