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The "it" in AI models is the dataset
- zer0gravity 2y agoThis insight makes one wonder if the same thing applies to humans as well. Are we just the sum of our experiences? Or the architectures of our brains are much more complex and different so that they have more influence on the outputs for the same inputs?
- cal85 2y agoI think it's the latter. We may well have some subsystems that work like LLMs or other current AIs, but the overall system of a human mind seems to work in a fundamentally different way, as it's able to make good creative choices (such as the next word to say) without looking at lots of options. Consider a chess engine that plays at grandmaster level, i.e. a human grandmaster can sometimes beat it. Even though it's not the best chess engine in the world, it simulates billions of possible scenarios to decide each move. Yet the grandmaster can still beat it sometimes, even though he clearly isn't thinking about billions of possible scenarios. (On the question of whether human brains may in fact unconsciously process billions of possibilities when deciding a chess move, using some neurological process we haven't discovered, I've heard David Deutsch argue this would be thermodynamically impossible as it would require far more energy than the brain consumes.) So the human grandmaster's brain must be doing something else that we don't understand. I think a similar comparison applies with how an LLM and a human choose the next word to say. An LLM has to run a giant statistical search for candidates. Humans seem to be doing something else.
- famouswaffles 2y ago>An LLM has to run a giant statistical search for candidates. Humans seem to be doing something else. LLMs don't work this way.
- cal85 2y agoCould you elaborate? If my understanding of this is significantly off then I’d appreciate if you could explain.
- famouswaffles 2y agoI mean there's no search. They compute probabilities but it's not a lookup table.
- disgruntledphd2 2y agoThis makes me sad, not because I disagree with it, but because it's basically common wisdom in the statistical and ML communities (of practitioners). In my experience, the only people who think architecture/model choice makes a huge difference are n00bs and academics. That being said, definitely if you use a linear model (like lasso) vs a tree based model (like XGBoost), you'll see differences, but once you have a flexible enough model and a lot of data, training time and inference complexity tend to become better ways to make a model choice.
- jstummbillig 2y agoWhy does it make you sad? It seems intuitiv and simple. And in reality of course the optimisation part is not trivial. What would we better if the "it" was more complicated?
- macilacilove 2y agoPossibly because being in the business of trying to turn iq edge into money, not data edge into money.
- michaelt 2y agoIt used to be that people would get into these fields thinking ML would need specifically human insights, deep thinking, and philosophical insights about the nature of consciousness. You would get into natural language modelling because you had a deep love of language. Because you think you're close to figuring language out in a systematic way, with just a few years more study. There's a certain sadness, I think, in the revelation that the robots don't need the expertise of humanity's greatest experts and masters, they just need us to click all the squares that contain a motorcycle.
- xanderlewis 2y agoHow do you know? We’re not there yet.
- disgruntledphd2 2y ago
- Eisenstein 2y agoAs a hobbyist having trained models for different use cases ranging from object detection and recognition to text completion to image generation, the best advice has consistently been to curate and annotate your dataset as perfectly as you can before worrying about anything else. A small, well-curated, well-annotated dataset will always be orders of magnitude better than a gigantic one with even a tiny percentage of mislabeled features or bad/wrong data. Hyperparameters and such can be fiddled with once you know you are on the right the track and in the scheme of things are relatively minor for most purposes. Of course, this advice gets routinely ignored as people spend countless hours fussing over how to set certain flags and grabbing as much data as possible, then carelessly throwing it all together and training it. Then, wondering why the model does things they don't want, they go back to messing with the parameters again. It is a giant pain in the ass but you have to spend the time sitting in front of the screen going through the data and removing things and tagging things and making sure that the details are right. This is really what makes the good models good and the rest mediocre.
- 0xDEADFED5 2y agothe 15T tokens that got thrown at Llama-3 didn't seem to hurt. Will be interesting to see how well Phi-2 holds up with it's more curated approach, hopefully they don't get disappeared like WizardLM 2 =)
- Eisenstein 2y ago"The quality of the prompts used in SFT and the preference rankings used in PPO and DPO played a crucial role in the performance of the aligned models. Meta's team carefully curated this data and performed multiple rounds of quality assurance on annotations provided by human annotators." * https://www.unite.ai/everything-you-need-to-know-about-llama-3-most-powerful-open-source-model-yet-concepts-to-usage/ https://www.unite.ai/everything-you-need-to-know-about-llama...
- redwood 2y agoThis is where software developers have a huge role to play: build software that invites user experiences that label as part of the user flow
- sampo 2y agoAlon Halevy, Peter Norvig, and Fernando Pereira (2009): The Unreasonable Effectiveness of Data https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/35179.pdf https://static.googleusercontent.com/media/research.google.c...
- 0xDEADFED5 2y agoRylan Schaeffer (2023): Pretraining on the Test Set Is All You Need https://arxiv.org/abs/2309.08632 https://arxiv.org/abs/2309.08632
- iNic 2y agoThe only thing this glosses over is RL. I guess you can see agents interacting in environments as a type of "dataset", but it _feels_ different.
- chrisdirl 2y agoIs the secret sauce also tied to the generation distribution which can differ from the dataset distribution e.g. RLHF?
- redwood 2y agoAKA "group think"
- mnk47 2y agoYi Tay's response (chief scientist at Reka AI, ex-Google Brain researcher): https://twitter.com/YiTayML/status/1783273130087289021 https://twitter.com/YiTayML/status/1783273130087289021 >not true, especially for language. if you trained a large & deep MLP language model with no self-attention, no matter how much data you'll feed it you'll still be lacking behind a transformer (with much less data). will it get to the same point? i don't think so. your tokens cannot even see each other in a raw MLP. >on the other hand, tiny tweaks to transformers may not matter as much as data/compute. sure. but it's also not very accurate to say "architecture research" does not matter and "makes no difference". i hear this a lot about how people use this to justify not innovating at the architecture level. >the truth is the community stands on the shoulder of giants of all the arch research that have been done to push the transformer to this state today. >architecture research matters. many people just take it for granted these days.
- ahartmetz 2y agoWell, both can be true if you interpret the "it" as "the secret sauce / competitive advantage". A good architecture is a necessary but not sufficient condition for success, but everybody uses more or less the same currently, so data makes the difference. Until the next improvement in architecture.
- nkozyra 2y agoOr until we run out of data that actually differentiates the models
- segmondy 2y agoI do argue that the IT is the architecture. We have pretty much had all the data that these LLMs were trained on for a long time. The game changer was the architecture not the data. Unless of course you are on the code is data camp ;).
- empath-nirvana 2y agoProbably the "it" is whatever one model has that other models don't have. When everyone is using the same architecture, then the data makes the difference. If everyone has the same data, then the architecture makes the difference. It sounds pretty obvious to say that the difference is whatever is different, but isn't that literally what both sides of this argument are saying? edit: I do think that what the original linked essay is saying is slightly subtler than that, which is that _given_ that everyone is using the same transformer architecture, the exact hyperparameters and fine tuning that is done matters a lot less than the data set does.
- rambambram 2y ago> It is a giant pain in the ass but you have to spend the time sitting in front of the screen going through the data and removing things and tagging things and making sure that the details are right. This is really what makes the good models good and the rest mediocre. In some other comment I read this. Sounds very much like a curation thing. And now I'm wondering; isn't this part already covered by a lot of human beings now interacting with ChatGPT and the like? My uneducated guess is that a company can scrape the whole world wide web and also have all the low quality content that comes with it, but then strengthen/curate their data and/or model by having it interact with humans? You give this thing a prompt, it comes up with some obvious nonsense, and then you as a human correct this by 'chatting' with it?
- Hendrikto 2y agoPeople typically ask LLMs about things they DON‘T know about or understand. So they are not qualified to assess the validity of their answers. Which is exactly why hallucination is such a big problem.
- eru 2y ago> People typically ask LLMs about things they DON‘T know about or understand. So they are not qualified to assess the validity of their answers. Eh, you can still often (!) figure out whether what the LLM says makes sense. Just like you can often figure out whether a human is bullshitting, by fact checking with other sources, or going over their reasoning.
- CuriouslyC 2y ago"Fixing" low quality data with RLHF is a waste of time. By that point it's already poisoned the model distribution, and all you're doing is steering it away from catastrophic failure cases. Start with the best data you can, and task train ("rlhf") behavior not preference.
- ttpphd 2y agoYeah when you use OpenAI you are giving them free labor for data curation.
- andy99 2y agoYes, and it's what people seem to ignore when they talk about dethroning GPT4 as the top LLM. It's good data expressly developed for training the behaviors they want that keeps them ahead, all the other stuff (other training and filtering web data) has much less of an impact. See also "You won't train a better model from your desk: https://news.ycombinator.com/item?id=40155715 https://news.ycombinator.com/item?id=40155715
- CuriouslyC 2y agoI don't think GPT4 is the top LLM, it's good at coding and good at understanding poorly written prompts but its high level prompt following and creativity are not great. GPT4 likes to answer a particular way and when your question matches up with that it'll seem very smart, but when it doesn't the rails it is on are very obvious.
- teekert 2y agoI don’t get this: “What that means is not only that they learn what it means to be a dog or a cat, …“ We don’t have any dataset of dog or cat experience right? OP probably means that he models learns wat a dog or cat is, right? I find the whole piece somewhat vague btw. No real insights if you ask me. Sure if all you put in is a dataset, that should be all you get out. What’s surprising (worth HN) here?
- omnicognate 2y ago> OP probably means that he models learns wat a dog or cat is, right? Yes, "What it means to be" does appear to be meant that way and it didn't occur to me to interpret it the other way. > Sure if all you put in is a dataset, that should be all you get out. What's surprising (worth HN) here? You put in a particular choice of nn architecture as well as the dataset. The insight (to the extent that it is insightful, and true) is that the architecture doesn't affect the results you get much compared to the dataset.
- teekert 2y agoOk the first thing must be just my non-native speaker mind then. The second: still fills like Duh. It’s what these models are meant to do right? Form an internal representation of the relations hidden in the data. It’s what complex systems are, they hold models of reality and use those to predict. That is in fact what Claude Shannon meant with his definition of information. Idk maybe I’m getting it wrong.
- empath-nirvana 2y ago> “What that means is not only that they learn what it means to be a dog or a cat, …“ I think he's referring to the famous paper: "What is it like to be a bat" https://en.wikipedia.org/wiki/What_Is_It_Like_to_Be_a_Bat%3F https://en.wikipedia.org/wiki/What_Is_It_Like_to_Be_a_Bat%3F
- troq13 2y agoWeak argument for something everyone already knew. Nice you work at openAI, I guess.
- pyinstallwoes 2y agoSo "it" is the collective unconscious of humanity? The egregore of us all, our collective spirit? I see.
- bilsbie 2y agoHas anyone tried removing an entire concept from a dataset and seeing if the LLM can reason its way into the concept? I think that would be a really cool experiment. There are probably some really good candidate concepts that just take a small leap of reasoning to reach. But off the top of my head maybe multiplication? Or the concept of zero. Maybe the wheel? Edit: if anyone is interesting in doing this kind of stuff, hit me up. (Email in profile). I want to start doing these kinds of things as a side project.
- andy99 2y agoThere was one where they tried to remove Harry Potter... Who's Harry Potter? Approximate Unlearning in LLMs https://arxiv.org/abs/2310.02238 https://arxiv.org/abs/2310.02238 See also The Boy Who Survived: Removing Harry Potter from an LLM is harder than reported https://arxiv.org/abs/2403.12082v1 https://arxiv.org/abs/2403.12082v1
- queuebert 2y agoI want to see an LLM that generates answers without the letter 'e', like the novel Gadsby by Ernest Vincent Wright.
- eru 2y agoIf you had one that was character based (instead of the weird encoding they tend to use), you could directly sample without e. Though I'm not sure its output would make much sense, and you might have to use beam search (or something like backtracking). I wonder how you would train a model to directly speak without e. Perhaps you use the general model like above with beamsearch, and then train a new model to directly predict the first models beamsearched-predictions.
- pk-protect-ai 2y agoThat is what I have repeated so many times in the last 2 years over and over. I consider Yi Tay's response [1] a mere technicality that is actually irrelevant. What is relevant is how predictable "interpolatable" the data are, how predictable we are. 1. https://twitter.com/YiTayML/status/1783273130087289021 https://twitter.com/YiTayML/status/1783273130087289021
- tilt_error 2y agoIs this a surprise? Isn't this exactly what Naftali Tishby has been talking about [1]. [1] https://www.youtube.com/watch?v=XL07WEc2TRI https://www.youtube.com/watch?v=XL07WEc2TRI
- dapf 2y ago[dead]
- tppiotrowski 2y agoI took the Andrew Ng Coursera machine learning course in 2015 and to this day I still remember him saying this in one of the videos. At the time he was talking about various versions/optimizations of gradient descent but he essentially said that tweaking the algorithm will only make your model ~1% better while doubling the amount of training data will have a substantially larger impact (use any old algorithm but just throw more data at the problem). That's why it was already evident back then that Google, Facebook, etc were sitting on a goldmine because in the long run those with the most data, not the brightest PhDs will win this race.
- jncfhnb 2y agoThere’s some enormous caveats to this. The model architecture is 100% the thing that makes LLMs special. You would not get this doing token prediction with word2vec. The model sizes are also hugely important. Adding billions of parameters does introduce the capability to fit to new features. The models eventually reach saturation of how much they can fit to. There’s reason to believe that current LLMs are underfit to what their sizes could theoretically utilize, but it could also be that the optimization algorithms are simply not capable of easily and efficiently utilizing another 2x data to fill out the space. Doubling the model size, on the same training data, and letting it be even more underfit could result in a better model.
- HarHarVeryFunny 2y ago> That's why it was already evident back then that Google, Facebook, etc were sitting on a goldmine because in the long run those with the most data, not the brightest PhDs will win this race. So far it doesn't seem to be panning out that way though. Companies such as OpenAI, Anthropic and Reka don't have any special internal sources of data, yet all have trained SOTA models. Probably the main reason for this is that data type/quality matters more than quantity, which is why most of these companies are now using self-generated synthetic data. The companies/institutes that will have a data advantage are those that have private datasets consisting of a different type (or maybe higher quality?) of data than publicly available, but this seems more likely to be in specialized domains (medical, etc), rather than what is useful for general intelligence. I assume that, longer term, we'll have better AI architectures capable of realtime learning, and then the focus may switch on-the-job training and learning ability, rather than data.
- tadala 2y agoAh the nature vs nurture debate, we meet again! Give me a Neural Net in its first epoch and I shall mold it into anything!
- deleted 2y ago[deleted]