8 ms·

I don't really understand the subject matter enough, so I apologize in advance for the meta-comment... The author mentions that he would maybe have written thi

• chessgecko 3y ago
  I think maybe its because he didn't have experimental results that show that it worked. Not a knock against the author, there are just so many things that seem like good ideas that don't end up working well in practice, a paper like this without results is hard to value.
  • mlsu 3y ago
    Yes, definitely. If he tried to have it published, the lack of experimental results would definitely be a glaring error. But this is still scientific communication. It's really nice that it's legible! > Even though softmax1 is facially quite boring, I’m 99.44% sure that it will resolve the outlier feedback loop that’s making quantization the subject of cascades of research. If you want to run some experiments and prove me right, DM me on Twitter and we’ll get a paper going. I'm guessing that in the stodgy world of science, a communication like this might happen over lunch at a conference, limited to a small clique of researchers who are zealously guarding their next paper. Who could blame them, publish or perish! But someone will probably test this theory out (after my read, it will probably happen in llama.cpp with preliminary results on GPT-2 by next week) and achieve results, and it will happen quickly and legibly to the outside world, because this was published openly and without all of the pretension that formal science (tm) has. If it works, it works. Stuff like this is the soul of the internet. Sharing knowledge and making it legible for all.
    • deleted 3y ago
      [deleted]
    • light_hue_1 3y ago
      There's a perfectly good venue for this communication: a workshop. Workshop submissions often don't need evidence. They just need a small kernel to spur discussion. Without experiments, there is no hope of publishing this in anything more than a workshop. Nor should there be.
  • WithinReason 3y ago
    Then again, if you don't have access to giant compute clusters you can't test this, so it's either a blog post or nothing. I believe the outlier problem that this solves only appears for very large models.
    • janalsncm 3y ago
      That isn’t true at all. Train a smaller model on a smaller dataset. You can even train on your laptop. It’s definitely feasible. This is just a proof of concept, it doesn’t need to beat state of the art.
      • WithinReason 3y ago
        Maybe I edited my comment too late.
        • janalsncm 3y ago
          > I believe the outlier problem that this solves only appears for very large models. Any reason to believe this? The author never mentioned it, and I can’t think of any other a priori reason why it should be true.
          • WithinReason 3y ago
            See figure 1: https://arxiv.org/pdf/2208.07339.pdf https://arxiv.org/pdf/2208.07339.pdf Outliers appear at model size 6.7B and are not present at 2.7B
            • janalsncm 3y ago
              Sure, emergent properties can arise as parameters increase. Everyone knows that. That’s a much less specific claim than to say that the benefit of modifying softmax can only arise as an emergent property after N parameters, and therefore the benefit can only be evaluated on models above a certain size. To my understanding the author of TFA isn’t suggesting the same issue as the one in your linked paper.
• Der_Einzige 3y ago
  This is why folks like gwern have their own research published this way, i.e. his analysis of GPT-3: https://gwern.net/gpt-3 https://gwern.net/gpt-3 We call him an "independent AI researcher" because his google scholar is "bland" compared to many academics who play the academia game - https://scholar.google.com/citations?user=yk1QMowAAAAJ&hl=en https://scholar.google.com/citations?user=yk1QMowAAAAJ&hl=en
• _Microft 3y ago
  > This paper It's not a paper. It's an idea that sounds plausible, presented in a highly entertaining form.
• doliveira 3y ago
  Nah, scientific papers are supposed to be precise and technical. This reads like those quite frequent suggestions here of switching all equations in papers to plain English or code: it honestly comes from a place of ignorance, and I say that as basically a layman myself. What should be encouraged is for academics to blog about their research as well. It would even help when recruiting and onboarding new members. Right now the sociological and economical incentives don't promote this at all.
  • mlsu 3y ago
    Well, I'm not so sure. It seems to me that someone could perfectly well devise an experiment based off of this (another poster chastised me for saying paper, so) blog post. Equations are perfectly clear. I was able to follow his reasoning perfectly well. I cannot say the same for so many papers (tm) that I've read. Mostly in a similarly computational (though non- deeplearning) applied math domain.
  • lofatdairy 3y ago
    I agree with everything you say. Though papers really are a bit too hard to read sometimes, but I'd argue it's often not for an overly technical tone so much as writers cutting out a lot of background material for brevity and assumed familiarity. >What should be encouraged is for academics to blog about their research as well. It would even help when recruiting and onboarding new members. Right now the sociological and economical incentives don't promote this at all. I will add onto this that a lot of journals have been pushing for video abstracts and "plain English" abstracts. For the most part I don't see these too often but when they're there they're appreciated, and I vaguely recall that someone found that citations go up when they're used (specifically plain English, I don't think anything has been on video abstracts). There are a lot of good blogs for computational academic subjects (ml, bioinformatics, comp neuro, etc) but I see less for bio and non-software engineering. Math and physics seems to have some really notable blogs, but beyond what gets posted to HN and linked further on those blogs, I can't comment.
  • r3trohack3r 3y ago
    There was this sociologist who had written a paper for us all to read ahead of time. I started to read the damn thing, and my eyes were coming out: I couldn’t make head nor tail of it! I figured it was because I hadn’t read any of the books on the list. I had this uneasy feeling of “I’m not adequate,” until finally I said to myself “I’m gonna stop, and read one sentence slowly so I can figure out what the hell it means.” So I stopped-at random-and read the next sentence very carefully. I can’t remember it precisely, but it was very close to this: “The individual member of the social community often receives his information via visual, symbolic channels.” I went back and forth over it, and translated. You know what it means? “People read.” Then I went over the next sentence, and realised that I could translate that one also. Then it became a kind of empty business: “Sometimes people read; sometimes people listen to the radio,” and so on, but written in such a fancy way that I couldn’t understand it at first, and when I finally deciphered it, there was nothing to it. -- Feynman I disagree. After going through quite a few research papers in my time, I've found the best are the ones that are direct and to the point. Many papers I've spent many hours/days trying to unravel just to realize the concepts were straightforward, not very novel, and there wasn't much of real substance to the paper. Meanwhile, some of the most impactful papers I've read are direct and to the point. Kadmellia, Bitcoin, BitTorrent, DynamoDB, Firecracker, etc. It seems like, when you have something of substance to say, you say it. When you don't you overcompensate by falling back on building an intricate puzzle of jargon and convoluted equations in an attempt to make what you're saying sound far more important than it really is. As LLMs get better, I look forward to the day where every journal has a standard LLM filter you're required to apply to your paper that unravels all of this nonsense and rewrites it a more straightforward way, if not to directly publish than just for the editors to verify there isn't a simpler way to convey your ideas. I suspect that if we had an EIL5 filter for most journal articles, we'd discover that a majority of the words that get published have very little substance at all.
• deleted 3y ago
  [deleted]
• nico 3y ago
  It would be amazing if academia started replacing papers with videos + code I want to see: an explainer of the science/ideas/experiments/hipothesis And instructions on how to reproduce the experiments/results Some YouTubers are going in this direction
  • janalsncm 3y ago
    +1 to including code with your paper. It improves reproducibility and transparency. There’s even a well-known website dedicated to this purpose. For the rest of it I don’t care. As long as researchers understand what’s going on, that’s what matters.
    • worthless-trash 3y ago
      I'm not an academic, but some of the notation and terminology they use makes me want to hunt them down and 'clockwork orange their eyes open' until they can show me how their math is "intended" to work. Inconsistent math notatation in papers along with vague terms in descriptions makes me so mad.
  • herval 3y ago
    oh god, please, no more videos...
  • saiojd 3y ago
    Most papers already have code, and videos are very common.
    • nico 3y ago
      Videos showing some result, but almost never a video of someone explaining the thing they are doing When they include good videos, they really stand out
• Legend2440 3y ago
  Counterargument: this blogpost is worthless. You get all the way to the end and then find out he hasn't actually tried it, not even on a toy model. It's just a neat idea he thinks will work.
  • ambrozk 3y ago
    Why would that make it worthless?
    • Legend2440 3y ago
      Because until he tries it, who knows if it works? There are a thousand papers out there making minor tweaks to the transformer architecture. 99% of them are also worthless and forgotten.
      • debugnik 3y ago
        > Because until he tries it, who knows if it works? That's precisely what he shared this for, though. So someone willing to train a model with this tweak tries it.
        • deleted 3y ago
          [deleted]
    • PoignardAzur 3y ago
      Among other reasons, because the decoder-only version of the original transformer architecture has proven weirdly resistant to these kinds of hacks and clever optimizations. Ideas like sparse attention, tree attention, residual attention, etc, all sound good on paper, but when researchers try to reproduce them they either find no results or results that don't scale. Even AliBi is turning out to be less powerful than scaled-down positional embeddings. It's almost a bitter lesson on its own: you can't beat the original transformer. Optimizations that do stick around tend to be the ones that preserve the original algorithm but help with caching or memory accesses.
    • 3y ago
• Waterluvian 3y ago
  To finish the author’s analogy: Blog posts are written by those who arrive first. In a weird way my mental model is: blog posts are the recon team discovering a new idea. They might have errors. They might be incomplete. Maybe they’re outright wrong. Stakes are lower as it took less effort to get there and less loss if a position is abandoned. Then papers are authored, often much later, and they’re the regulars coming in to fortify a newly captured idea. They provide (or at least are supposed to) rigor to the idea. A fortification of a position that we decide is worth holding. Yeah, this analogy is probably sloppy. But in my brain there’s an eternal conflict against ignorance as we keep advancing into the unknown.
• pessimizer 3y ago
  > The tone is self-effacing, it does not have an "ego" the way scientific papers tend to have. I can't imagine judging scientific papers based on whether the author might be looking down on me, or thinks he knows better than me. > if we were "allowed" to cite research that reads like this Maybe you're looking down on yourself? You can cite anything you want to cite.
  • caddemon 3y ago
    Well if you yourself are trying to publish in a scientific venue you can't always cite exactly what you want to cite. Though it's probably uncommon for a peer reviewer to ask for a specific citation to be removed, the review process absolutely does affect the references list, and expectations about this process affect it doubly so.
    • simonster 3y ago
      In ML, no one is going to police your citation list. I've cited some weird stuff in my papers, including ideas from tweets and random quotes from Jeff Dean. It's never been a problem.
• TigeriusKirk 3y ago
  Are most AI papers even published beyond arxiv anyway?
• Method-X 3y ago
  I can see AI being used to make scientific papers more approachable like this.
• baby 3y ago
  There isn't much difference between a blog and a whitepaper, in that people tend to write blogs more casually and whitepaper more seriously (and some academics event only accept things that look more serious). But a good writer can write great articles in whatever format they wish.
• herval 3y ago
  I learned more from this post than a thousand papers. Amazing writing!
• thomasahle 3y ago
  > it does not have an "ego" the way scientific papers tend to have. What do you call it when somebody takes the time to write about "a big discovery" they've made, but don't take the time to check if somebody else already did it? It's not like it's in some forgotten paper nobody has seen. It's in Pytorch itself. Also this: "I’m 99.44% sure that it will resolve the outlier feedback loop that’s making quantization the subject of cascades of research."
• simonster 3y ago
  It's interesting, because as a scientist who reads and writes these kinds of papers, my first impression was: This guy has a pretty big ego or is otherwise badly miscalibrated if he believes his genius idea has a "99.44%" chance of preventing outlier activations without doing any experiments.
  • misthop 3y ago
    Not ego, he's playing on the old Ivory Soap slogan "99+44⁄100% Pure" https://en.m.wikipedia.org/wiki/Ivory_(soap) https://en.m.wikipedia.org/wiki/Ivory_(soap)
• fogof 3y ago
  A lot of thoughts in this thread on what academic papers are or should be, let me give my own opinion as a person who tries to write papers. Papers should be structured like fractals - that is, they should be "self-similar". The main text of the paper after the introduction should go into all the necessary details demonstrating the origins of the idea and proving that it has value. Then the introduction section should summarize all this, and take a less rigorous tone. The abstract should be a summary of the introduction. And then the title should summarize the abstract. If you really have a lot of technical work to do, maybe you can write a super long appendix and have the main body summarize that. I myself probably spend as much time reading paper introductions as I do reading paper bodies, which means that probably 90% of the papers I read, I only read the introduction. I do this because I enjoy it more - I like new ideas, and the intros are a great way to get a lot of them. This blog post reads like a great paper introduction to me. It's easy to trick yourself into believing something is easy though, so an academic paper would have to back this up with an experiment.