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Were RNNs all we needed?

• tehsauce 2y ago
  I haven’t gone through the paper in detail yet but maybe someone can answer. If you remove the hidden state from an rnn as they say they’ve done, what’s left? An mlp predicting from a single token?
  • statusfailed 2y ago
    I only had a quick look, but it looks like they tweaked the state update so the model can be run with parallel scan instead of having to do it sequentially.
  • jfcoa 2y ago
    It doesn't completely remove it, it removes certain dependencies on it so that it can be computed by parallel scan, there is still a hidden state. It bears some similarity to what was done with Mamba.
  • bunderbunder 2y ago
    They didn't remove the hidden state entirely, they just removed it from the input, forget and update gates. I haven't digested the paper either, but I think that in the case of a GRU this means that the hidden state update masking (z_t and r_t in the paper's formulas) only depends on the new input, not the input plus the prior hidden state.
  • _0ffh 2y ago
    The trick is to make sure the recursive dependency stays linear, that's how you enable parallel training.
• hydrolox 2y ago
  Betteridge's law of headlines?
  • woah 2y ago
    For paper titles, the law is that the answer is always "yes"
    • bunderbunder 2y ago
      Not always, I think? Opinions probably differ, for example, on John Backus's paper "Can programming be liberated from the Von Neumann style?" Many fans of functional programming would say the answer is yes, but Backus himself expressed less enthusiasm in interviews later in his life. I think the important point, though, is that academic papers and newspaper articles are not the same, and titles in the form of questions function differently in the two domains. Journalists tend to use titles like these to dissemble and sensationalize. When academics use these kinds of titles for peer-reviewed articles, it's because they really are asking an honest question. Backus was doing it in his paper. The authors of this paper are doing the same. They end the paper by re-iterating the question before launching into a discussion of the limitations that prevent them from reaching any firm conclusions on the answer to this question.
    • nephanth 2y ago
      More like "we aren't sure, but we have good reasons not to exclude the possibility"
• hiddencost 2y ago
  Note Yoshua Bengio in the author list. This shouldn't be taken lightly.
  • auggierose 2y ago
    And this is where science breaks down.
    • hotspot_one 2y ago
      Not really, because 1) Yoshua's reputation would take a hit if this paper were bullshit, so he has extrinsic motivation to make it good 2) Yoshua has enough experience in the field to know what is going on in the field, you don't have to ask if he forgot about a certain architecture or the work of a certain research group which would contradict his findings-- if such work exists and is credible, it is very likely to be discussed in the paper. 3) This test answers something a leader in the field thinks is important enough for them to work on, else he wouldn't be involved. Also note, the poster said the paper shouldn't be taken lightly. That doesn't mean we need to take it blindly. It only means we cannot dismiss it out of hand, if we have a different view we would need substantive arguments to defend our view. I've overturned the field leader several times in science, but that's only because I acknowledged what they got right and that they were indeed the person who got it right.
      • DAGdug 2y ago
        “ I've overturned the field leader several times in science” Either that makes you a field leader yourself, or you did it for trivial things, or you’re BSing. Which one is it?
        • exe34 2y ago
          there's a big space between leader and trivial. it's entirely possible to point out the top leader in your field is wrong on ten things over a career, without becoming the top leader yourself.
• imjonse 2y ago
  To their credit, the authors (Y. Bengio among them) end the paper with the question, not suggesting they know the answer. These models are very small even by academic standards so any finding would not necessarily extend to current LLM scales. The main conclusion is that RNN class networks can be trained as efficiently as modern alternatives but the resulting performance is only competitive at small scale.
  • phkahler 2y ago
    >> These models are very small even by academic standards so any finding would not necessarily extend to current LLM scales. Emphasis on not necessarily. >> The main conclusion is that RNN class networks can be trained as efficiently as modern alternatives but the resulting performance is only competitive at small scale. Shouldn't the conclusion be "the resulting competitive performance has only been confirmed at small scale"?
    • imjonse 2y ago
      yes, that is clearer indeed. However S4 and Mamba class models have also performed well at small scale and started lagging with larger models and larger context sizes, or at particular tasks.
• xnx 2y ago
  It's curse and a blessing that discussion of topics happens in so many different places. I found this comment on Twitter/X interesting: https://x.com/fchollet/status/1841902521717293273 https://x.com/fchollet/status/1841902521717293273 "Interesting work on reviving RNNs. https://arxiv.org/abs/2410.01201 https://arxiv.org/abs/2410.01201 -- in general the fact that there are many recent architectures coming from different directions that roughly match Transformers is proof that architectures aren't fundamentally important in the curve-fitting paradigm (aka deep learning) Curve-fitting is about embedding a dataset on a curve. The critical factor is the dataset, not the specific hard-coded bells and whistles that constrain the curve's shape. As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime."
  • islewis 2y ago
    > "As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime." I haven't fully ingested the paper yet, but it looks like it's focused more on compute optimization than the size of the dataset: > ... and (2) are fully parallelizable during training (175x faster for a sequence of length 512 Even if many types of architectures converge to the same loss over time, finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale.
    • teruakohatu 2y ago
      > Even if many types of architectures converge to the same loss over time, finding the one that converges the fastest is quite valuable given the cost of running GPU's at scale. This! Not just fastest but with the lowest resources in total. Fully connected neural networks are universal functions. Technically we don’t need anything but a FNN, but memory requirements and speed would be abysmal far beyond the realm of practicality.
      • actionfromafar 2y ago
        Unless we could build chips in 3D?
• m11a 2y ago
  It’d be nice to see more of how this compares to Mamba. Looks like, in performance, they’re not leagues apart and it’s just a different architecture, not necessarily better or worse?
  • yazzku 2y ago
    Look at the memory consumption diagram on page 6. It looks like you're basically getting the same running time for less memory usage.
• dsamarin 2y ago
  The name of the paper contrasts with the paper that spawned Transformer architecture, which itself is a reference to the song "All You Need Is Love" by the Beatles. https://en.wikipedia.org/wiki/Attention_Is_All_You_Need https://en.wikipedia.org/wiki/Attention_Is_All_You_Need
  • vundercind 2y ago
    I eagerly await the backlash to suggesting any one thing is all you need, the first shot of which shall surely be titled: “‘All you need’ Considered Harmful”
    • ants_everywhere 2y ago
      Surely the universe is all you need though
      • radarsat1 2y ago
        Interstellar taught me that love transcends the universe. Ergo..
• marcosdumay 2y ago
  R == Recurrent From theory the answer to the question should be "yes", they are Turing complete. The real question is about how to train them, and the paper is about that.
  • baanist 2y ago
    Why aren't AI researchers automating the search for efficient architectures?
    • kelseyfrog 2y ago
      The search space is all off too wide, difficult to parameterize, and there is a wide gap between effective and ineffective architectures - ie: a very small change can make a network effectively DOA.
      • hedgehog 2y ago
        Notably architecture search was popular for small vision nets where the cost of many training runs was low enough. I suspect some of the train-then-prune approaches will come back, but even there only by the best funded teams.
    • ks2048 2y ago
      https://en.wikipedia.org/wiki/Neural_architecture_search https://en.wikipedia.org/wiki/Neural_architecture_search
    • ActorNightly 2y ago
      There has been some work, but the problem is that its such a massive search space. Philosophically speaking, if you look at how humans came into existence, you could make an argument that the process of evolution from basic lifeforms can be represented as one giant compute per minute across of all of earth, where genetic selection happens and computation proceeds to the next minute. Thats a fuckload of compute. In more practical terms, you would imagine that an advanced model contains some semblance of a CPU to be able to truly reason. Given that CPUs can be all NAND gates (which take 2 neurons to represent), and are structured in a recurrent way, you fundamentally have to rethink how to train such a network, because backprop obviously won't work to capture things like binary decision points.
• logicchains 2y ago
  The model in the paper isn't a "real" RNN due making it parallelizable, for same the reasons described in https://arxiv.org/abs/2404.08819 https://arxiv.org/abs/2404.08819 , and hence is theoretically less powerful than a "real" RNN (struggles at some classes of problems that RNNs traditionally excel at). On the other hand, https://arxiv.org/abs/2405.04517 https://arxiv.org/abs/2405.04517 contains a "real" RNN component, which demonstrates a significant improvement on the kind of state-tracking problems that transformers struggle with.
  • robertsdionne 2y ago
    These are real RNNs, they still depend upon the prior hidden state, it’s just that the gating does not. The basic RNN equation can be parallelized with parallel prefix scan algorithms.
• bob1029 2y ago
  > Transformers required ~2.5x more training steps to achieve comparable performance, overfitting eventually. > RNNs are particularly suitable for sequence modelling settings such as those involving time series, natural language processing, and other sequential tasks where context from previous steps informs the current prediction. I would like to draw an analogy to digital signal processing. If you think of the recurrent-style architectures as IIR filters and feedforward-only architectures as FIR filters, you will likely find many parallels. The most obvious to me being that IIR filters typically require far fewer elements to produce the same response as an equivalent FIR filter. Granted, the FIR filter is often easier to implement/control/measure in practical terms (fixed-point arithmetic hardware == ML architectures that can run on GPUs). I don't think we get to the exponential scary part of AI without some fundamentally recurrent architecture. I think things like LSTM are kind of an in-between hack in this DSP analogy - You could look at it as FIR with dynamic coefficients. Neuromorphic approaches seem like the best long term bet to me in terms of efficiency.
  • wslh 2y ago
    ELI5: Could you explain what neuromorphic approaches mean, and how they contribute to AI/AGI? My first impression as a layperson (probably wrong) is that this approach resembles ideas from the book "The Society of the Mind", where the system isn't just simulating neurons but involves a variety of methods and interactions across "agents" or sub-systems.
    • bob1029 2y ago
      Neuromorphic mostly just means "like how the brain works". It encompasses a variety of software & hardware approaches. The most compelling and obvious one to me is hardware purpose-built to simulate spiking neural networks. In the happy case, SNNs are extremely efficient. Basically consuming no energy. You could fool yourself into thinking we can just do this on the CPU due to the sparsity of activations. I think there is even a set of problems this works well for. But, in the unhappy cases SNNs are impossible to simulate on existing hardware. Neuronal avalanches follow power law distribution and meaningfully-large ones would require very clever techniques to simulate with any reasonable fidelity. > the system isn't just simulating neurons but involves a variety of methods and interactions across "agents" or sub-systems. I think the line between "neuron" and "agent" starts to get blurry in this arena.
• PunchTornado 2y ago
  To me this is further evidence that these LLMs learn only to speak English, but there is no reasoning at all in them. If you simplify a lot and obtain the same results and we know how complex the brain is.
  • quantadev 2y ago
    Every LLM expert on the planet agrees LLMs are doing "reasoning". No one says they have feelings or qualia, but we all know there's definitely genuinely artificial reasoning happening. What LLMs have shown both Neuroscience and Computer Science is that reasoning is a mechanical process (or can be simulated by mechanical processes) and is not purely associated only with consciousness.
    • roboboffin 2y ago
      I'm not sure that's true at all. There are several well known researchers that say LLMs are in fact not doing reasoning.
      • quantadev 2y ago
        Those are all the people that have not yet decoupled "reasoning" from "consciousness" in their own way of thinking. It's admittedly hyperbolic to say "everyone". I love hyperbole on HN. :)
        • roboboffin 2y ago
          For example, papers like this call into question whether or not a LLM can plan: https://arxiv.org/html/2409.13373v1 https://arxiv.org/html/2409.13373v1 This is a basic form of reasoning, to plan out the steps needed to execute something.
          • quantadev 2y ago
            Planning, by definition, takes multiple reasoning steps. A single LLM inference is a fundamental single reasoning step, but it's a reasoning step nonetheless. It's like I'm saying a house is made of bricks. You can build a house of any shape out of bricks. But once bricks have been invented you can build houses. The LLM "reasoning" that even existed as early as GPT3.5 was the "brick" with which highly intelligent agents can be built out of, with no further "breakthroughs" being required. The basic Transformer Architecture was enough and already has the magical ingredient of reasoning. The rest is just a matter of prompt engineering.
• adamnemecek 2y ago
  Yes, all machine learning can be interpreted in terms of approximating the partition function. This is obvious when one considers the connections between Transformers, RNNs, Hopfield networks and the Ising model, a model from statistical mechanics which is solved by calculating the partition function. This interpretation provides us with some very powerful tools that are commonplace in math and physics but which are not talked about in CS & ML. I'm working on a startup http://traceoid.ai http://traceoid.ai which takes this exact view. Our approach enables faster training and inference, interpretability and also scalable energy-based models, the Holy Grail of machine learning. Join the discord https://discord.com/invite/mr9TAhpyBW https://discord.com/invite/mr9TAhpyBW or follow me on twitter https://twitter.com/adamnemecek1 https://twitter.com/adamnemecek1
• mkaic 2y ago
  I strongly enjoy the simplicity of their "minGRU" architecture. It's basically just: class MinGRU(nn.Module): def __init__(self, token_size, hidden_state_size): self.token_to_proposal = nn.Linear(token_size, hidden_size) self.token_to_mix_factors = nn.Linear(token_size, hidden_size) def forward(self, previous_hidden_state, current_token): proposed_hidden_state = self.token_to_proposal(current_token) mix_factors = torch.sigmoid(self.token_to_mix_factors(current_token)) return torch.lerp(proposed_hidden_state, previous_hidden_state, mix_factors) And since the proposed hidden states and mix factors for each layer are both only dependent on the current token, you can compute all of them in parallel if you know the whole sequence ahead of time (like during training), and then combine them in linear time using parallel scan. The fact that this is competitive with transformers and state-space models in their small-scale experiments is gratifying to the "best PRs are the ones that delete code" side of me. That said, we won't know for sure if this is a capital-B Breakthrough until someone tries scaling it up to parameter and data counts comparable to SOTA models. One detail I found really interesting is that they seem to do all their calculations in log-space, according to the Appendix. They say it's for numerical stability, which is curious to me—I'm not sure I have a good intuition for why running everything in log-space makes the model more stable. Is it because they removed the tanh from the output, making it possible for values to explode if calculations are done in linear space? EDIT: Another thought—it's kind of fascinating that this sort of sequence modeling works at all. It's like if I gave you all the pages of a book individually torn out and in a random order, and asked you to try to make a vector representation for each page as well as instructions for how to mix that vector with the vector representing all previous pages — except you have zero knowledge of those previous pages. Then, I take all your page vectors, sequentially mix them together in-order, and grade you based on how good of a whole-book summary the final vector represents. Wild stuff. FURTHER EDIT: Yet another thought—right now, they're just using two dense linear layers to transform the token into the proposed hidden state and the lerp mix factors. I'm curious what would happen if you made those transforms MLPs instead of singular linear layers.
  • immibis 2y ago
    This architecture, on the surface, seems to preclude the basic function of recognizing sequences of tokens. At the very least, it seems like it should suffer from something like the pumping lemma: if [the ][cat ][is ][black ] results in the output getting close to a certain vector, [the ][cat ][is ][black ][the ][cat ][is ][black ][the ][cat ][is ][black ] should get even closer to that vector and nowhere close to a "why did you just repeat the same sentence three times" vector? Without non-linear mixing between input token and hidden state, there will be a lot of linear similarities between similar token sequences...
• trott 2y ago
  My feeling is that the answer is "no", in the sense that these RNNs wouldn't be able to universally replace Transformers in LLMs, even though they might be good enough in some cases and beat them in others. Here's why. A user of an LLM might give the model some long text and then say "Translate this into German please". A Transformer can look back at its whole history. But what is an RNN to do? While the length of its context is unlimited, the amount of information the model retains about it is bounded by whatever is in its hidden state at any given time. Relevant: https://arxiv.org/abs/2402.01032 https://arxiv.org/abs/2402.01032
  • mkaic 2y ago
    The counterargument here is that you can just scale the size of the hidden state sufficiently such that it can hold compressed representations of whatever-length sequence you like. Ultimately, what I care about is whether RNNs could compete with transformers if FLOPs are held constant—something TFA doesn't really investigate.
    • psb217 2y ago
      Well, that's what Transformer already does... One problem with the scaling you're describing is that there would be a massive amount of redundant information stored in hidden activations during training the RNN. The hidden state at each time step t in the sequence would need to contain all info that (i) could be useful for predicting the token at time t and (ii) that could be useful for predicting tokens at times >t. (i) is obvious and (ii) is since all information about the past is transferred to future predictions through the current hidden state. In principle, Transformers can avoid storing redundant info in multiple hidden states at the cost of having to maintain and access (via attention) a larger hidden state at test/eval time.
      • mkaic 2y ago
        > there would be a massive amount of redundant information stored in hidden activations Is there a way to prove this? One potential caveat that comes to mind for me is that perhaps the action of lerping between the old state and the new could be used by the model to perform semantically meaningful transformations on the old state. I guess in my mind it just doesn't seem obvious that the hidden state is necessarily a collection of "redundant information" — perhaps the information is culled/distilled the further along in the sequence you go? There will always be some redundancy, sure, but I don't think that such redundancy necessarily means we have to use superlinear methods like attention.
• fhdsgbbcaA 2y ago
  We really need a [preprint] flag for unreviewed papers.
  • lgessler 2y ago
    IMHO reviews are almost indistinguishable from noise at the AI conferences I'm familiar with these days anyway, so I don't see much of a value add.
    • fhdsgbbcaA 2y ago
      Sad state of affairs, people are incentivized to get more papers and citations at all costs, and quality be damned. An AI Winter is not a great an idea, but an AI Autumn may be beneficial. Just have no major AI conferences for ‘25, perhaps only accept really high tier literature reviews.
• limapedro 2y ago
  This is such a interesting paper, sadly they don't have big models, I'd like to see a model trained on TinyStories or even C4 since it should be faster than the transformer variant and see how it compares.
• deleted 2y ago
  [deleted]
• charlescurt123 2y ago
  I find the entire field lacking when it comes to long-horizon problems. Our current, widely used solution is to scale, but we're nowhere near achieving the horizon scales even small mammal brains can handle. Our models can have trillions of parameters, yet a mouse brain would still outperform them on long-horizon tasks and efficiency. It's something small, simple, and elegant—an incredible search algorithm that not only finds near-optimal routes but also continuously learns on a fixed computational budget. I'm honestly a bit envious of future engineers who will be tackling these kinds of problems with a 100-line Jupyter notebook on a laptop years from now. If we discovered the right method or algorithm for these long-horizon problems, a 2B-parameter model might even outperform current models on everything except short, extreme reasoning problems. The only solution I've ever considered for this is expanding a model's dimensionality over time, rather than focusing on perfect weights. The higher dimensionality you can provide to a model, the greater its theoretical storage capacity. This could resemble a two-layer model—one layer acting as a superposition of multiple ideal points, and the other layer knowing how to use them. When you think about the loss landscape, imagine it with many minima for a given task. If we could create a method that navigates these minima by reconfiguring the model when needed, we could theoretically develop a single model with near-infinite local minima—and therefore, higher-dimensional memory. This may sound wild, but consider the fact that the human brain potentially creates and disconnects thousands of new connections in a single day. Could it be that these connections steer our internal loss landscape between different minima we need throughout the day?
  • aDyslecticCrow 2y ago
    Yes... The field lacks the HOLY GRAIL (long-horizon problems). But we don't need a mouse-brain to sort spam emails. The Hail Mary 2B+ parameter models and above are still niche uses of these algorithms (too heavy to run practically). There is plenty of room for clever and small models running on limited hardware and datasets to solve useful problems and nothing more. Models that change size as needed have been experimented with, but they are either too inefficient or difficult to optimize at a limited power budget. However, I agree that they are likely what is needed if we want to continue to scale upward in size. I suspect the real bottleneck is a breakthrough in training itself. Backpropagation loss is too simplistic to optimize our current models perfectly, let alone future larger ones. But there is no guarantee a better alternative exists which may create a fixed limit to current ML approaches.
• kgbcia 2y ago
  Decision trees is all we needed
• vandahm 2y ago
  I made a RNN for a college project because I was interested in obsolete historical technology and I thought I needed to seize the opportunity while it lasted, because once I was out of school, I'd never hear about neural networks ever again. Mine worked, but it was very simple and dog slow, running on my old laptop. Nothing was ever going to run fast on that thing, but I remember my RNN being substantially slower than a feed-forward network would have been. I was so confident that this was dead technology -- an academic curiosity from the 1980s and 1990s. It was bizarre to see how quickly that changed.
  • alkonaut 2y ago
    I feel old. I made my masters thesis on RNN's for learning dynamic systems e.g. for control purposes (quite a novelty at the time, around 2000). We wrote the backprop in C++ and ran it over night. Yes it was slow as hell with the tiny gradients. The network architectures were e.g. 5 or 10 neurons in a single hidden layer. NN's were a tiny subject that you were lucky to find courses in. Then closed my eyes for two seconds and looked at the subject again in 2015. Wow.
• gdiamos 2y ago
  RNNs always had better scaling law curves than transformers. BPTT was their problem
• Smerity 2y ago
  Excited to see more people working on RNNs but wish their citations were better. In 2016 my team from Salesforce Research published our work on the Quasi-Recurrent Neural Network[1] (QRNN). The QRNN variants we describe are near identical (minGRU) or highly similar (minLSTM) to the work here. The QRNN was used, many years ago now, in the first version of Baidu's speech recognition system (Deep Voice [6]) and as part of Google's handwriting recognition system in Gboard[5] (2019). Even if there are expressivity trade-offs when using parallelizable RNNs they've shown historically they can work well and are low resource and incredibly fast. Very few of the possibilities regarding distillation, hardware optimization, etc, have been explored. Even if you need "exact" recall, various works have shown that even a single layer of attention with a parallelizable RNN can yield strong results. Distillation down to such a model is quite promising. Other recent fast RNN variants such as the RWKV, S4, Mamba et al. include citations to QRNN (2016) and SRU (2017) for a richer history + better context. The SRU work has also had additions in recent years (SRU++), doing well in speech recognition and LM tasks where they found similar speed benefits over Transformers. I note this primarily as the more data points, especially when strongly relevant, the better positioned the research is. A number of the "new" findings from this paper have been previously explored - and do certainly show promise! This makes sure we're asking new questions with new insights (with all the benefit of additional research from ~8 years ago) versus missing the work from those earlier. [1] QRNN paper: https://arxiv.org/abs/1611.01576 https://arxiv.org/abs/1611.01576 [2] SRU paper: https://arxiv.org/abs/1709.02755 https://arxiv.org/abs/1709.02755 [3]: SRU++ for speech recognition: https://arxiv.org/abs/2110.05571 https://arxiv.org/abs/2110.05571 [4]: SRU++ for language modeling: https://arxiv.org/abs/2102.12459 https://arxiv.org/abs/2102.12459 [5]: https://research.google/blog/rnn-based-handwriting-recognition-in-gboard/ https://research.google/blog/rnn-based-handwriting-recogniti... [6]: https://arxiv.org/abs/1702.07825 https://arxiv.org/abs/1702.07825
• hdivider 2y ago
  I still find it remarkable how we need such an extreme amount of electrical energy to power large modern AI models. Compare with one human brain. Far more sophisticated, even beyond our knowledge. What does it take to power it for a day? Some vegetables and rice. Still fine for a while if you supply pure junk food -- it'll still perform. Clearly we have a long, long way to go in terms of the energy efficiency of AI approaches. Our so-called neural nets clearly don't resemble the energy efficiency of actual biological neurons.
  • Arch485 2y ago
    It's even less! A lot of those vegetables and rice go into powering your heart, muscles, organs, etc. and only a fraction is used for the brain. Maybe the future of AI is in organic neurons?
  • jjmarr 2y ago
    Food is extremely dense in energy. 1 food calorie is about 1.1 Watt-hours. A hamburger is about 490 Wh. An AI model requires 0.047 kWh = 47 Wh to generate 1000 text responses.[1] If an LLM could convert hamburgers to energy, it could generate over 10000 prompt completions on a single hamburger. Based on my own experience, I would struggle to generate that much text without fries and a drink. [1] https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption https://www.theverge.com/24066646/ai-electricity-energy-watt...
    • hdivider 2y ago
      During that time, your brain would do far more than just that text generation though, beyond what we even know scientifically. But yes, food energy could be useful for AI. A little dystopian potentially too, if you think about it. Like DARPA's EATR robot, able to run on plant biomass (although potentially animal biomass too, including human remains): https://en.wikipedia.org/wiki/Energetically_Autonomous_Tactical_Robot https://en.wikipedia.org/wiki/Energetically_Autonomous_Tacti...
      • jjmarr 2y ago
• lettergram 2y ago
  In 2016 & 2017 my team at Capital One built several >1B parameter models combining LSTMs with a few other tricks. We were able to build generators that could replicate any dataset they were trained on, and would produce unique deviations, but match the statistical underpinnings of the original datasets. https://medium.com/capital-one-tech/why-you-dont-necessarily-need-data-for-data-science-48d7bf503074 https://medium.com/capital-one-tech/why-you-dont-necessarily... We built several text generators for bots that similarly had very good results. The introduction of the transformer improved the speed and reduced the training / data requirements, but honestly the accuracy changed minimal.
• moi2388 2y ago
  Yes, and it’s hardly surprising, since the Chinese room thought experiment is completely wrong; that is in fact exactly how you learn something.
• theanonymousone 2y ago
  I remember that, the way I understood it, Transformers solved two major "issues" of RNNs that enabled the later boom: Vanishing gradients limiting the context (and model?) size and difficulty in parallelisation limiting the size of the training data. Do we have solutions for these two problems now?
  • ebalit 2y ago
    Transformers can also fetch at any moment any previous information that become useful. RNN are constantly updating and overwriting their memory. It means they need to be able to predict what is going to be useful in order to store it for later. This is a massive advantage for Transformers in interactive use cases like in ChatGPT. You give it context and ask questions in multiple turns. Which part of the context was important for a given question only becomes known later in the token sequence. To be more precise, I should say it's an advantage of Attention-based models, because there are also hybrid models successfully mixing both approaches, like Jamba.
    • visarga 2y ago
      You could theoretically run the input twice, allowing the model to correlate later tokens with previous ones. It would fix the problem with not knowing what information to retain. A more complicated approach would train the RNN to request replaying some earlier data when needed. A great thing about RNNs is they can easily fork the state and generate trees, it would be possible to backtrack and work on combinatorial search problems. Also easier to cache demonstrations for free in the initial state, a model that has seen lots of data is not using more memory than a model starting from scratch.
      • imjonse 2y ago
        Something like this? https://hazyresearch.stanford.edu/blog/2024-07-01-jrt https://hazyresearch.stanford.edu/blog/2024-07-01-jrt
        • visarga 2y ago
• lccerina 2y ago
  "Was all along a scheme by Google to sell more tensor processing units that didn't run RNNs well?"
• scotty79 2y ago
  The only strength of transformers is that they can run once for each token and they can pass to themselves intermediate state as they solve your problems. They have to conceal it in tokens that look to humans like a part of the response. It's obvious why the newest toy from openai can solve problems better mostly by just being allowed to "talk to itself" for a moment before starting the answer that human sees. Given that, modern incarnation of RNN can be vastly cheaper than transformers provided that they can be trained. Convolutional neural networks get more visual understanding by "reusing" their capacity across the area of the image. RNN's and transformers can have better understanding of a given problem by "reusing" their capacity to learn and infer across time (across steps of iterative process really). When it comes to transformer architecture the attention is a red herring. It's just more or less arbitrary way to partition the network so it can be parallelized. The only bit of potential magic is with "shortcut" links between non adjacent layers that help propagate learning back through many layers. Basically the optimal network is deep, dense (all neurons connect with all belonging to all preceding layers) that is ran in some form of recurrence. But we don't have enough compute to train that. So we need to arbitrarily sever some connections so the whole thing is easier to parallelized. It really doesn't matter which unless we do in some obviously stupid way. Actual inventive magic part of LLMs possibly happens in token and positional encoders.
• tadala 2y ago
  Everyone wants to use less compute to fit more in, but (obviously?) the solution will be to use more compute and fit less. Attention isn't (topologically) attentive enough. All these RNN-lite approaches are doomed, beyond saving costs, they're going to get cooked by some other arch—even more expensive than transformers.
  • falcor84 2y ago
    Would you mind expanding upon your thesis? If that compute and all those parameters aren't "fitting" the training examples, what is it that the model is learning, and how should that be analyzed?
    • ithkuil 2y ago
      I think there are two distinct areas. One is the building of the representations, which is achieved by fitting. The other area is loosely defined as "computing" which is some kind of searching for a path through representation space. All of that is wrapped in a translation layer that can turn those representations into stuff we humans can understand and interact with. All of that is achieved to some extent by current transformer architectures, but I guess some believe that they are not quite as effective at the "computation/search" stage.
      • falcor84 2y ago
        But how does it get good at "computing"? The way I see it, we either program them to do so manually, or we use ML, at which case the model "fits" the computation based on training examples or environmental feedback, no? What am I missing?
        • ithkuil 2y ago
          the distinction is fuzzy indeed, especially if any thing that you "program in manually" has some parameters that are learned. Conceptually we already have parts of the model that are not learned: the architecture of the model itself.
• Sysreq2 2y ago
  Guys, I’m gonna stop this before it gets out of hand: All we need is love and a shit ton of compute. Everything else is just details.
• cs702 2y ago
  I finally got around to reading this. Nice paper, but it fails to address a key question about RNNs: Can RNNs be as good as Transformers at recalling information from previous tokens in a sequence? Transformers excel at recalling info, likely because they keep all previous context around in an ever-growing KV cache. Unless proponents of RNNs conclusively demonstrate that RNNs can recall info from previous context at least as well as Transformers, I'll stick with the latter.