6 ms·
LLMs aren't "trained on the internet" anymore
- zer00eyz 2y agoYou sir get an F in history and the industry does too. Does no one remember why expert systems fell apart? Because you have to keep paying experts to feed the beast. Because they are bound to the whims and limitations of experts. Making up data isnt going to get us there, we already failed with this method ONCE. Open AI's bet with MS and the resignation of all the safety people says everything you need to know. MS gets everthin up to AGI... IF you thought you were close, if you thought that you were going to get there with a bigger model and more data then you MIGHT want MS's money. And MS had its own ML folks publish papers with "hints of AGI", The google engineer saying "it's AGI" before getting laughed at... I suspect that everyone at OpenAI was high on their own supply. That they thought AGI would emerge, or sapience, or sentience if they shoved enough data at it. I think the safety minding folks leaving points to the fact that they found the practical limitations. Show me the paper that has progress on hallucination. Show me the paper that doubles effectiveness and halves the size. These are where we need progress for this to become more than grift, than NFT's.
- sebzim4500 2y agoThey aren't going to show you any papers at all, they like money.
- falcor84 2y ago>Show me the paper that doubles effectiveness and halves the size. LLM's have pretty clearly been the most rapidly advancing technology in the history of humankind. Are you not entertained?!
- solidasparagus 2y ago> Does no one remember why expert systems fell apart? Many of the current generation of AI experts mostly either did not pay great attention to the history of AI or they believe this time is completely different. They would do well to spend more time learning about history. However, your view doesn't strike me as correct either. Expert system fell apart because the world was more complex than researchers realized and enumeration was essentially discovered to be infeasible (more or less as you say). But the impossibility of enumerating the world isn't news, everyone knows "the bitter lesson". And this isn't the past - now everyone on earth carries around a computer, a video camera and a microphone. They talk to each other through the internet. Remote workers screens' are recorded. Billions of vehicles with absurd numbers of sensors are roaming around the world. More of the arenas that matter to humanity are digital and thus effective domains for automated exploration and data generation. The information about how the world operates exists or can be generated, the only real question is how to get your hands on it.
- discreteevent 2y ago> The information about how the world operates exists or can be generated, the only real question is how to get your hands on it. I'm sure I could read all the information for an astrophysics course in a relatively short time. Understanding it is a different matter.
- solidasparagus 2y agoUnderstanding is a loaded term. But large transformers seem pretty good at learning from datasets (of more or less any modality) to they extent that they can create useful new datapoints and allow you to work with existing datapoints in useful, structured ways.
- Jensson 2y agoTransformers main usefulness is translation, including translations from technical lingo to natural languages and vice versa, or code to natural language etc. That was their targeted usecase when originally made and that is what most people use them for. This is what you use them for that doesn't require hallucinating. They don't understand so they can't replace technical experts, they can just translate a bit back and forth but you still need an expert who understands the domain since translations isn't enough for a layperson to replace an expert. The other use case is to make them hallucinate tropey stories and concepts for brainstorming, this isn't nearly as useful though due to it adding so much low quality stuff here when it hallucinates.
- zer00eyz 2y ago> The information about how the world operates exists or can be generated The hubris of mathematics. At what scale does whether prediction become 100 percent accurate? How large of a model do you need, and how big of a computer to run it? Do we thing that reducing the world to a model and feeding it through (what isnt even close to a model) of "thought" or "interaction" or ... what ever you want to bill and LLM as is going to be any more accurate than weather prediction?
- YeGoblynQueenne 2y ago>> Does no one remember why expert systems fell apart? There were many reasons. One of them was the "Knowledge Acquisition Bottleneck", but that was not about the cost of paying experts, rather the cost of creating and maintaining a potentially very large knowledge base (i.e. one big mother of a database of production rules). Also, the fact that many experts' knowledge is tacit and not easily formalisable. Modern machine learning began in the 1980's as an effort to overcome the Knowledge Acquisition Bottleneck. Accordingly many early machine learning approaches were designed to learn production rules for expert systems. Decision trees, one of the staple classifiers in data science, come from that era; you can tell, because decision trees are a symbolic, logic-based "model". There were other problems with expert systems, e.g. their infamous "brittleness". But modern, statistical machine learning systems, are also criticised for "brittleness" too. There were also purely political reasons and nothing to do with science or technology considerations. Then there was the 5th Generation Computer Project, and the AI winter, and then there were no more expert systems. The journal of Expert Systems with Applications is still alive and well, on the other hand, although it mostly publishes on machine learning and neural nets these days. With an occasionally cool article, like one about Wolf Colony Optimisation I spotted recently. Too tired to look for links now, sorry.
- numpad0 2y agoI've seen some infographics that shows LLMs practically need to see same data 4 times or less, and once is fine too(trained for one epoch). And I was like, y'saying, it's a zipped list of edge cases...
- Jensson 2y agoIts a zip with lossy compression for text. First useful lossy text compression algorithm we have made.
- imtringued 2y agoIt was trained that way, so it would be weird if it wasn't.
- fnordpiglet 2y agoExperts weren’t the bottleneck on expert systems it was the systems weren’t particularly adaptive, were too rigid, weren’t able to make abductive conclusions, and the user interfaces were way too difficult in situ. LLMs actually tackle quite a lot of these issues FWIW but I wouldn’t look at them as a replacement for expert systems. Instead they’re probably what will make them useful by providing a natural human interface and a way of providing an abductive “reasoning” ability ontop of traditional expert systems.
- HL33tibCe7 2y ago> Admittedly, this used to be true! And is still mostly true. But it’s increasingly becoming less true. So the headline is bullshit then. The thrust of this article is essentially “LLMs are trained on the internet, _but wait_, they are also trained on other stuff in these very rarified and specific cases”. So even you concede that, LLMs are still, for the most part, trained on the internet.
- neilv 2y agoI used ta do Web corpus. I still do. But I used ta, too. (Apologies to Mitch Hedberg. https://www.youtube.com/watch?v=VqHA5CIL0fg https://www.youtube.com/watch?v=VqHA5CIL0fg )
- gmuslera 2y ago"trained" and putting everything in the same bag hides the possibility that not all training data have the same weight, confidence, or even deep tags to differentiate between an expert opinion from a 4chan post.
- deleted 2y ago[deleted]
- hn_throwaway_99 2y agoI agree the article title is clickbait. But the article makes the good point that people often say LLMs are "trained on the Internet" to imply all of the statistical problems with that (e.g. the type of content on the Internet, and populations who are more likely to post on the Internet, are not representative samples of knowledge). This article's point is more I feel that so much is being invested in private data that it's no longer really fair to make that implication by default.
- emporas 2y agoSomewhat of a clickbait, but "it's increasingly becoming less true" exponentially. The human population produces written data, exponentially, but in a less steep slope than LLMs by themselves. Human text may double every year, LLM generated text may double every day, or every second.
- mrkramer 2y agoData is valuable so for example; I don't understand why Reddit and Stack Overflow gave their valuable data to OpenAI and Google for pennies, when they could've made their own chatbots and beat OpenAI and Google at their own game.
- pizza 2y agoStack Overflow did try this but imo their LLM wasn't that good
- mrkramer 2y agoSo after few iterations they decided to give up and get the quick buck? So shortsighted from them.
- solardev 2y agoDo they have much time left? Seems like once the answers are all scraped and trained, that site wouldn't be able to survive long anyway?
- bandrami 2y agoThe next version of libfoo released after 2023 will have a new set of options in /etc/foo.conf and at some point a human being who knows that will have to answer a question about it for an LLM to know that.
- reducesuffering 2y agoNo, future LLMs will ingest the codebase, any docs, and be able to answer the question anyway. That is, if the LLM didn’t generate the code base itself…
- KeplerBoy 2y agoThere's a lot of wishful thinking going on here isn't it? All these models still regularly fail to provide me with correct code to use even the most common open source libraries (stuff like numpy and matplotlib), even though there were certainly trained on that code.
- RecycledEle 2y agoWow. They quoted me in the article.
- RecycledEle 2y agoThis is one of the longest articles anyone ever wrote to prove me wrong. I agree that LLMs are not 100% trained on Internet posts, but that they are mostly trained on good Internet posts. When I ask an LLM a question I expect a simulation if a good answer from an Internet discussion board specializing in that topic.
- mattgreenrocks 2y agoIn true HN comment style, the article comes out both with guns blazing and leads with a “well actually…” :)
- logrot 2y agoThe dream is collapsing.
- benreesman 2y agoAs Something of a Vagueposter Myself, I’ll bite. Which dream is collapsing? I’m not disputing it, legitimately curious which of several collapsing dreams you mean.
- rzzzt 2y agoIt's the title of a Hans Zimmer song from the Inception soundtrack, pretty intense!
- benreesman 2y agoCool sidebar, thanks for reminding me. I find Zimmer soundtracks to be some of the best coding music and many if not most of them are on e.g. Spotify.
- benreesman 2y agoI think this post makes a few good points, certainly the parabolic trajectory Scale seems to be on is at least suggestive if not conclusive that there’s a lot more going on now than just big text crawls. And Phi-3 is something else, even from relatively limited time playing with it, so that’s useful signal for anyone who hadn’t looked at it yet. Wildly cool stuff. It seems weird to not mention Anthropic or Mistral or FAIR pretty much at all: they’re all pretty clearly on more modern architectures at least as concerns capability per weight and Instruct-style stuff. I’m part of a now nontrivial group who regards Opus as basically shattering GPT-4-{0125, 1106}-Preview (which is basically the same as 4o for pure language modalities) on basically everything I care about, and LLaMA3 is just about there as well, maybe not quite Opus, comparable if you ignore trivially gamed metrics like MMLU. And I have no idea why we’re talking about GPT-5 when there’s little if any verifiable evidence it even exists as a training run tracking to completion. Maybe it is, maybe not, but let’s get a look at it rather than just assume that it’s going to lap the labs that are currently pushing the pace now?
- ein0p 2y agoHave you actually tried using Opus side by side with GPT4 on “work” related stuff? GPT4 is way better in my experience, to the point where I cancelled my Opus subscription after just a couple of months.
- deleted 2y ago[deleted]
- benreesman 2y agoI make an effort to use both and several high-capability open tunes every day (it’s not literally every day but I have keyboard shortcuts for all of them). Opus historically had issues with minor typographical errors, though recently that seems to not happen often, lots of very sharp people at Anthropic. So a month ago if I wanted something from Opus I’d run it through a cleanup pass courtesy of one of the other ones, but even my old standby dolphin-8x7 can clean up typos. 1106 can as well, but all else equal I don’t want to be sending my stuff to any black box data warehouse and I’m always surprised so many other sophisticated people don’t share the preference. My personal eyeball capability check is to posit a gauge symmetry and ask what it thinks the implied conserved quantity is, and I’ve yet to see Opus not crush that relative to anything else, including real footnotes. On coding I usually hand it a Prisma schema and ask for a proto3/gRPC definition that is a good way to interact with it, Opus in my personal experience also dominates there. If you have an example of a task that represents a counter example I’d be grateful for another little integration test for my personal ad-hoc model card. I want to know the best tool for every job.
- delusional 2y agoThis sounds terrible. We are paying PhD level experts to produce novel work exclusively available through an overly optimistic, lying, robot. What if those expert just published the stuff freely online instead. Surely that would be more productive and trustworthy. Reality is truly stupid.
- jltsiren 2y agoIs that different from a company doing R&D without releasing the results to the public? Experts working for private gain is the norm, not the exception. That kind of work also sounds incredibly boring. They probably have to pay their experts a lot more than it would normally cost to hire the same caliber of experts. Which would mean they are not generating very much private data.
- civilized 2y agoHopefully it'll all be available in a fire sale when these companies finally have to be stripped for parts.
- foreverobama 2y ago[dead]
- bossyTeacher 2y ago> We are paying PhD level experts to produce novel work exclusively available through an overly optimistic, lying, robot. We are not. Open AI is
- skissane 2y ago> We are paying PhD level experts to produce novel work exclusively available through an overly optimistic, lying, robot. Because nobody else is willing to pay them to do that. Research grants, peer review publications, etc, are not set up to reward stuff like “produce one thousand random examples of expressing thoughtful uncertainty with respect to questions relevant to your discipline for which there is currently no consensus answer” > What if those expert just published the stuff freely online instead. Surely that would be more productive and trustworthy. I’m sure many of them would be happy to do so if someone is paying them
- jacobsenscott 2y agoOpenAI etc will be paying irresistible sums of money to companies that promised to keep data private. Think slack (and their recent "opt out" fiasco), Atlassian, Dropbox...
- amelius 2y ago"It's easier to ask for forgiveness" is the main modus operandi nowadays ...
- barbariangrunge 2y agoDiscord…
- sdfgtr 2y ago> While some of this is for annotation and ratings on data that came from the web or LLMs, they also create new training data whole-hog: The article states that this human data is PhDs, poets, and other experts but my recollection from some info about programming LLM training is that there was a small army of low paid Indian programmers feeding it with data. Even if it's actually experts now I have to wonder when that will switch to 3rd worlders making $1/hour.
- deleted 2y ago[deleted]
- stefan_ 2y agoI love the marketing upstart attitude, but indeed, the reality of "PhDs, poets and subject matter experts expanding the frontiers of AI" is much more likely to be the "Amazon cashierless supermarket" experience. The problem with hiring that group of people is presumably that they are not poor enough to lack ambition in their career, which every dummy can spot from miles away is an utter dead end feeding some LLM.
- XorNot 2y agoIsn't it just curating an encyclopaedia though? The point is that LLM training is moving from "suck down the internet" to "consume an annotated and contextualised reference of the library of Congress". The difference between trusting 5 random people to tell you how they think quantum mechanics works versus asking 5 presently publishing physicists.
- deleted 2y ago[deleted]
- jamilton 2y agoHere are the job postings from the mentioned company, Outlier. https://boards.greenhouse.io/outlier https://boards.greenhouse.io/outlier
- fewald_net 2y ago
- deleted 2y ago[deleted]
- astrea 2y agoIt's fascinating to watch the whole "Data is the new oil" thing grow and morph into something truly horrible.
- freeone3000 2y agoWhat have been your experiences with oil?
- hole_in_foot 2y agoFor an company with open in its name, we sure don't know what data openai trains its models on. Why?
- bilbo0s 2y agowe sure don't know what data openai trains its models on. Why? ??? Um, that's kind of obvious. Because they want to make money. I think the point of the article is that it may get to the point where these internal datasets are so much more valuable than the sea of fake nonsense on the open internet that the practice of keeping the internal datasets private will become a no-brainer. Who is willing to give up billions of dollars just 'cuz? Even if you can't run a company, one of the big companies will happily pay you for access to that data. There's no way you make the data public if that data is able to provide an edge in training. In fact, let's be realistic, the more of an edge it provides, the more money it will be worth.
- surfingdino 2y ago> For example, if your model is hallucinating because you don’t have enough training examples of people expressing uncertainty, or biased because it has unrepresentative data, then generate some better examples! Or, as the case may be... humans are biased? Also "generate some better examples" sounds like fudging data to fit the expected outcome. It smells of clutching at straws hoping to come up with something before the world looses interests and investor money runs out. If you want to see how LLMs fail at coming up with original responses ask your favourite hallucinating bot to come up with fifty different ways of encouraging people to "Click the Subscribe button" in a YT video. Not only it will not come up with anything original, but it will simply start repeating itself (well, not itself, it will start repeating phrases found in YT video transcripts).
- apike 2y ago> Also "generate some better examples" sounds like fudging data to fit the expected outcome. LLMs are tools. As a tool author, you have certain desired outcomes for certain use cases. If the current data you’re training on isn’t giving you those outcomes, it is absolutely reasonable to "fudge" the data. This might mean reducing bias, or adding bias, or any number of nudges. Training an LLM is not a scientific study, it’s a product development effort.
- surfingdino 2y agoAgreed. However, you are then giving your tools to people who have none of that experience and understanding and apply it to the problems they are trying to solve without then taking a pause and checking the results against facts. There is a lot of trust in the outputs and little vigilance. A common reply to such concerns is "well, you should be able to spot incorrect information in the outputs" which is tricky if we are talking about education where by definition students are yet to learn correct answers or lower levels of career development, very much similar to education when they are learning on the job. The lack of ability to quote and trace sources of information used to construct output by an LLM is a major red flag for me, sensitive information leakage is another. They way LLMs are sold is irresponsible, they are sold as tools to solve problems, not as a thousand monkeys trying to type up the whole works of Shakespeare, which isv where we are at the moment.
- stephc_int13 2y agoThe current state of LLMs would be several orders of magnitude more impressive if they were only trained from data scrapped on the web. But this is not the reality of modern LLMs by a long shot, they are trained in increasingly large parts from custom built datasets that are created by countless paid individuals, hidden behind stringent NDAs. The author here seems to see that as a strength, an opportunity for unbounded growth and potential, I think this is the opposite, this approach is close to a gigantic whack a mole game, effectively unbounded, but in the wrong way.
- jackblemming 2y agoReminds me of the same issues with self driving. Seems like we need a completely different approach to solve these class of problems.
- deleted 2y ago[deleted]
- apike 2y agoYeah this is an interesting point. Other threads make the point about the "bitter lesson", and how expert-trained ML has historically not scaled, and human-generated LLM training data may just be repeating that dead end. Maybe so. Something that is new this time around, AFAIK, is that we haven’t previously had general ML systems that businesses and consumers are paying billions of dollars a year to use. So if, say, 10% of revenue goes back in to making better data sets every year, I can imagine continued improvement on certain economically valuable use cases – though likely with diminishing returns.
- preommr 2y ago> countless paid individuals, hidden behind stringent NDAs. If this is so prevalent, wouldn't there be a proportional amount of data leaks? If there any particular evidence, even of doubtful authenticity, of this being the case?
- Jensson 2y ago
- kazinator 2y ago> A dataset like “50,000 examples of Ph.Ds expressing thoughtful uncertainty when asked questions they don’t know the answer to” could be worth a lot more than it costs to produce. Those PHDs better up their negotiating skills then.
- remram 2y agoSo they're still rained on the Internet, just not only the public Internet anymore. What a clickbait title.
- fnordpiglet 2y agoI sort of hope we get a tech investment fueled WPA that simply pays skilled writers to write, and I hope they allow the body of work to be released by the authors to the public when there’s something of general value written. A wonderful irony of the training and development of superior language models could be the creation of a superior corpus of human authored work.
- deleted 2y ago[deleted]
- ProxCoques 2y ago> this train will keep rollin’ for a while yet. OK. And then what?
- poulpy123 2y agoI have the feeling that the future big steps in AI will not be related to data
- seesawtron 2y ago>So other than training ever-larger models on the same internet data, how can they make better LLMs? Training a multi-modal model that can integrate audio, visual, text and all sorts of data modalities to human level capabilities still remains an clear challenge. The bottleneck here is not the lack of data imo.