12 ms·

From deep to long learning?

• aqme28 3y ago
  It seems to me like these long context lengths are going to make a huge difference in the capabilities of things people are able to produce. Agents can use LLMs to subdivide a large corpus into manageable chunks. More context just makes them much much better at that. Regardless, for most programming tasks, I doubt my equivalent human context length is any better than 32k tokens.
  • jerpint 3y ago
    The problem is not the short term context length, but long term. If you want to do things like long term goal planning, keeping track of distant past events can be of high value
    • PaulHoule 3y ago
      I’d argue for planning that the answer is to couple the LLM to some other system rather than try to improve the LLM. Combinatorial optimization is a well-understood problem that is NP-complete in theory but practical to solve in practice. For doing math we might as well give the LLM a pocket calculator, why not couple it to a planner, theorem prover and similar tools?
      • pixl97 3y ago
        This is pretty much what people are testing now with Auto-GPT
      • pmoriarty 3y ago
        "For doing math we might as well give the LLM a pocket calculator, why not couple it to a planner, theorem prover and similar tools?" ChatGPT has already been hooked up to Wolfram Alpha.[1] For hooking up other things, see HuggingGPT and TaskMatrix.AI.[2][3] [1] - https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/ https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its... [2] - https://arxiv.org/pdf/2303.17580.pdf https://arxiv.org/pdf/2303.17580.pdf [3] - https://arxiv.org/pdf/2303.16434.pdf https://arxiv.org/pdf/2303.16434.pdf
      • btbuildem 3y ago
        Precisely. LLMs are the Perl of the future
      • sharemywin 3y ago
        That's the interesting thing. if LLM start to just become interfaces for other systems you could probably just use them to train smaller systems like in the style of alpaca and other recent LLMs.
    • taneq 3y ago
      Maybe that’s what our inner monologue is for… to cycle relevant context through regularly so it stays in scope. I mean, that’s not actually why we have one but it’d be cute.
  • PartiallyTyped 3y ago
    Our context is hierarchical, at multiple different resolutions, so I don't think it's comparable.
    • btbuildem 3y ago
      I've had good results using that approach with LLMs for domain-specific problem solving.
    • letitgo12345 3y ago
      Good chance so is OAI's https://arxiv.org/abs/2110.13711 https://arxiv.org/abs/2110.13711 (paper has the long context lead at OAI as a co-author)
    • XorNot 3y ago
      I'd say when you're actively trying to remember an event and "narrowing it down" mentally is a process that feels suspiciously like a dialogue with an LLM where you keep asking for the answer to be improved.
    • aqme28 3y ago
      I was trying to describe a hierarchy of LLMs as an agent. I don't think that's a uniquely human ability.
  • NhanH 3y ago
    Human expert relies in the “chunking” effect for expertise, which is mostly not part of the context length (working memory). For a pseudo-analogy, each human is fine-tuned from the raw intelligence (by training and education). In that way generic LLM probably can’t beat us yet so don’t despair!
    • onos 3y ago
      Any job focused on fact retrieval is at risk.
      • RandomLensman 3y ago
        Need to know that the facts exist in the first place, though.
        • orbifold 3y ago
          GPT-4 is already at a level that it has graduate level math and physics knowledge, with a level of recall that most students can only dream of.
          • RandomLensman 3y ago
            Do those actually matter in jobs? Most valuable facts I encounter are far more niche, often in someone's head and not written down, only stored privately somewhere etc.
      • awesomeMilou 3y ago
        What kind of job is only focused on fact retrieval?
        • fnordpiglet 3y ago
          Jeopardy contestant
        • istjohn 3y ago
  • jacquesm 3y ago
    'Superhuman' has many dimensions. It can be speed, it can be ability to retain a lot of information, it can be the ability to deal with a large quantity of information at once and many other dimensions besides. For the longest time Chess was considered a domain where computers could be dilettantes but could never dominate. Chess is now in 'superhuman' territory and likely we will never see a reversal because any insight that benefits humans also benefits computers but not the other way around. The fact that this is such a multi-dimensional problem is frequently overlooked in the debate about AI/AGI etc, it may not matter all that much if the non AGI AI is already superhuman on enough dimensions other than the ones that the 'but it isn't AGI' crowd cling to. The consequences are what matters, not the fine print or the implementation details, and those consequences are directly tied to the number of dimensions along which a computer can beat humanity. To give an example: if a chess program was 3000 Elo before but so slow that it would lose under competition rules then humans still dominated chess. Likewise if it would be only 2500 Elo but fast enough, it would still lose from the best humans. But for a large fraction of society it would have already moved out into 'superhuman' territory. And a couple of technological leaps of progress later and we're all looking at that AI as if it has moved into superhuman regions. This sort of thing will happen on many fronts, and all of those fronts are moving, if enough of them go past the threshold then whether it is AGI or not is irrelevant and for every person that threshold is at different points. Maybe a computer will be able to calculate faster and better than you can, maybe it will be able to translate text faster and better than you, maybe it will be able to organize information faster and better than you. At which point we cross the line into saying that it can think faster and better than you is hard, but we can see that we are getting close to that line without even knowing exactly where that line is.
    • pmoriarty 3y ago
      "Chess is now in 'superhuman' territory and likely we will never see a reversal because any insight that benefits humans also benefits computers but not the other way around." What is considered human is malleable. It is conceivable that humans will be enhanced in various biological and non-biological ways to a point that they can once again compete with computers.
      • 3y ago
  • kmeisthax 3y ago
    The problem with comparing LLM and human context length is that LLMs don't update their weights based on their input. The 32k of context that they do have is their only memory. Humans have multiple layers of memory and can recall things and concepts from years in the past - akin to millions of tokens' worth of recall in an LLM. Yes, that memory is extremely lossy, but it's there.
• cs702 3y ago
  This looks really interesting! If these guys succeed in bringing self-attention's computational cost down from O(n²) to O(n log n), that would be a huge win. The quadratic cost makes it very difficult to increase sequence length on current hardware. I'm going to take a closer look. There are other interesting ongoing efforts to increase sequence length. One that has worked for me is this dynamic routing algorithm, related to self-attention, that can handle sequences with 1M+ tokens in a single GPU: https://github.com/glassroom/heinsen_routing https://github.com/glassroom/heinsen_routing . Right now, you can take 1,000 sequences of hidden states computed by a pretrained transformer, each sequence with, say, 1024 tokens, concatenate them into a single ultra-long sequence with 1,024,000 hidden states, slap 1,024,000 position encodings on top, and feed the whole thing to that routing algorithm to predict the next token (or whatever other training objective you want to optimize for). It works. Search the README for "Very Long Sequences". If anyone here has other suggestions for working with long sequences (hundreds of thousands to millions of tokens), I'd love to learn about them.
  • famouswaffles 3y ago
    There are already linear attention advances. Gpt-4-32k is almost certainly using some form of flash attention. Attention isn't really O(n2) anymore.
    • cs702 3y ago
      My understanding is that FlashAttention's memory use is linear, or close to linear in practice, but computation is still O(n²). I'm unaware of anyone being able to apply FlashAttention on, say, a million tokens, because it must execute ~1/2 x 1,000,000^2 x n_head dot-products, each in a subspace with d_head dimensions. That's not exactly computationally cheap!
      • famouswaffles 3y ago
        No you're right. I mistook you. Compute isn't linear yet.
    • lucidrains 3y ago
• mark_l_watson 3y ago
  Interesting about Butterfly architecture for hardware FFT support. In the 1980s, DARPA provided two types of exotic hardware to the company I worked for: the first Connection Machine, and the Butterfly machine. I wrote Star Lisp code for the CM, but never touched the Butterfly machine. Off topic, but I am curious what hardware Apple will release in the future for more direct AI support. Their Core ML libraries working with Apple Silicon have been very effective so far. The next step would likely be a built in foundation LLM model, extending what they have supported with BERT models, etc.
  • cs702 3y ago
    > The next step would likely be a built in foundation LLM model, extending what they have supported with BERT models, etc. I'm thinking deeper. It wouldn't surprise me if self-attention itself becomes a primitive building block of future co-processors, e.g., with instructions and memory layouts engineered to make ultra-low-precision self-attention as compute- and memory-efficient as possible. I'm expecting LLMs with hundreds of billions and eventually trillions of parameters will be able to run locally on my laptop and mobile phone, in the not-too-distant future.[a] [a] If this sounds far-fetched, consider that you can already run LLMs with tens of billions of parameters on mobile phones: https://justine.lol/mmap/ https://justine.lol/mmap/
    • fpgaminer 3y ago
      > I'm expecting LLMs with hundreds of billions and eventually trillions of parameters will be able to run locally on my laptop and mobile phone, in the not-too-distant future Perhaps. There's been a lot of focus on training-compute optimal models in the industry. Rightfully so, as proofs of concept. That's what led to this perceived parameter count race in published models. But remember the other side of the scaling laws. For inference, which is what we want to do on our phones, it's better to be inference-compute optimal. That means smaller models trained for longer. As far as we know today there are no limits of the scaling laws. A 1B parameter model _can_ beat a 1T parameter model, if trained for long enough. Of course it's exponential, so you'd have to pour incalculable training resources into such an extreme example. But I find these extreme examples elucidating. My pet theory these days is that we'll discover some way of "simulating" multiple parameters from one stored parameter. We know that training-compute optimal models are extremely over-parameterized. So it isn't the raw capacity of the model that's important. It seems like during training the degrees of freedom is what allows larger models to be more sample efficient. If we can find a cheap way of having one parameter simulate multiple degrees of freedom, it will likely give us the ability to gain the advantages of larger models during training, without the inference costs later. I don't disagree that we're likely to see more and more parameter capacity from our devices. I'm just pointing out that the parameter count race is a bit of an illusion. OpenAI discovered the scaling laws and needed a proof of concept. If they could show AI reaching X threshold first, they could capture the market. The fastest way to do that is to be training-compute optimal. So they had to scale to 175B parameters or more. Now that it's proven, and that there's a market for such an AI, their and other's focus can be on inference-optimal models which are smaller but just as smart.
• skepticATX 3y ago
  Can someone with a better understanding than I have comment about the relationship between these results and this paper: https://arxiv.org/abs/2109.09115 https://arxiv.org/abs/2109.09115, which seems to demonstrate that a longer context length has diminishing returns?
  • rsfern 3y ago
    I’m not deeply familiar with all these papers, but two things stand out to me The model architectures are different, and in the very latest paper they scale these not-transformer models to sequence length of 64k, where the paper you linked only considers up to 8k
• raphlinus 3y ago
  The thing that stuck out to me is the assertion that FFT is poorly supported on modern GPU. That's surprising to me, as there's cuFFT offically supported by Nvidia, and vkFFT that achieves similar performance portably using compute shaders. I believe these are based on f32 math, so perhaps the potential win is using tensor cores to compute FFT at lower precision? It seems surprising to me that decomposing into matrix operations is the win here, it seems you'd do better writing a kernel that makes use of the cooperative matrix (aka WMMA, tensor core, simd_matrix) capabilities of the GPU.
  • svantana 3y ago
    Looking at the source paper, they are claiming a 2.2x speedup over cuFFT for convolutions, so it's not an earth-shattering gain, but still.
• Herval_freire 3y ago
  Someone in a previous comment on LLM research said that according to what he knew about LLM research that we were in a local maximum that no further improvement was likely possible. I disagreed with him, and this article is evidence that is in favor of my point. If research like this continues to move forward LLMs will improve at a rapid rate. Different threads attract different groups of people with different areas of expertise. So I will sort of reiterate this topic here as I'm interested. What are most people's thoughts on this "local maximum" thing have we actually hit a dead end? Especially given the proliferation of effort towards producing research like the one showed in the topic here.
  • ChatGTP 3y ago
    I mean I just read that article and it doesn't seem like a lot will change, sure it can summarize a whole book, or read a larger chunk of code to do thing with, but didn't really see it talk about taking things to "the next level" so to speak. The researchers are also, excited. We’re especially motivated by applications that could benefit from longer-sequence models – high-resolution imaging, new modalities of data, language models that can read entire books. Imagine giving a language model an entire book and having it summarize the plot, or conditioning a code generation model on all the code you’ve ever written. The possibilities are wild – and we’re excited.
    • Herval_freire 3y ago
      But this research came out mere weeks after the release of gpt-4. That is in itself rapid. If small incremental changes like this continue on a sort of monthly basis, the trendline points towards something that's not a dead end. That's my view of it. As with most technology there isn't necessarily always a constant influx of inflection points and paradigm shifts. Improvement will likely creep up on us incrementally. Suddenly one day its clearly more intelligent then a human and we can't point to when it happened.
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• edulix 3y ago
  Instead of long learning or long contexts, at some point artificial neural networks will have to transition to continuous/online learning - learn while using the network. This way, limitations are broken like they are in our minds. Similar to what Numenta HTM networks do, but scalable and performant for real use cases. BTW, perhaps human-like conscience emerge as a "self-attention-like" mechanism between context and learning. Just saying.
  • sr-latch 3y ago
    My hunch is that the next progression in AI will incorporate forward forward training
  • qumpis 3y ago
    Learn how? I think having infinite context is perfect - no need to learn on my data online and risk exposing it to others.
  • thomasahle 3y ago
    Alternatively we need the model to have a long term memory, and be able to load stuff to/from that while reading.
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• 3327 3y ago
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• Ozzie_osman 3y ago
  There will be those of us that understand how all these models work, and there will be those of us that simply use them.
  • istjohn 3y ago
    That's true of just about any technology. Most carpenters would fail to explain why their hammer drives a nail into wood instead of bouncing off, why it has a handle that extends past the hand's grip, why the head of the hammer neither shatters nor mushrooms over time, to say nothing of their nail guns and circular saws.
• AvAn12 3y ago
  With longer (50k+) context lengths, is this just becoming a new form of search?
  • jmole 3y ago
    I think the K,Q,V representation was what fundamentally gave rise to LLMs (from "Attention is all you need"), and I'm certain that it wouldn't have happened without the researchers having a background in search @ Google. Or in other words, it was always a new form of search.
    • bckr 3y ago
      [Astronaut looking at earth with the logo of superhuman AI superimposed] Wait, it’s all just search? [astronaut with gun] Always has been
  • intalentive 3y ago
    Yes, now we just need it to provide citations.
• jmole 3y ago
  Oddly enough, I was reading their paper just last night: https://arxiv.org/pdf/2302.10866.pdf https://arxiv.org/pdf/2302.10866.pdf I think we're going to see a lot more in the wavelet/convolution/fft space when thinking about how to increase context length. I think there's also a lot of room for innovation in the positional encoding and how it's represented in transformer models, it seems like people have been trying lots of things and going with what works, but most of it is like: "look, a new orthonormal basis!". Hyena sort of seems like the first step in moving to positional embeddings (or joint positional/attentional embeddings). Very cool work.
  • cs702 3y ago
    I agree this sort of approach looks promising. Maybe using FFTs recurrently to approximate convolutions with input-length filters is the way forward. It's a clever idea. I'm making my way through the paper. Don't fully understand it yet. The main issue I've seen with other wannabe-sub-quadratic-replacements for self-attention is that they all rely on some kind of low-rank/sparse approximation that in practice renders LLMs incapable of modeling enough pairwise relationships between tokens to achieve state-of-the-art performance. I'm curious to see if this kind of approach solves the issue.
    • inciampati 3y ago
      Does FFT not suggest an implicit kind of quantization to the dimension of the time space model? It's something that I have not understood about S4, Hippo, Hyena and other FFT-trick models.
      • cs702 3y ago
        FFT's inputs are discrete to begin with (evenly spaced samples in the time domain), so I kind of see what you're trying to say... but I don't fully understand the work yet. I'm slowly making my way, starting with getting acquainted with classic state-space models. That said, my sense is there's a significant difference between (a) learning to approximate the matrix of n×n interactions with more run-of-the-mill methods, such as some kind of linear decomposition with learned fixed coefficients, versus (b) learning to approximate the matrix of n×n interactions with dynamic methods that "find the most suitable decomposition for each sample on-the-fly," which is what these class of models appears to be doing. Apologies if all this sounds very hand-wavy; it's the best I can do at the moment.
• pmontra 3y ago
  Is this the way we work? We are told a fact a very few times and we remember it all life long, no 32k or 32M context. I think that they are following the easy path, much like the Giga Hertz race in CPU, and will hit a wall. Maybe the wall will be so far away that it will give us an AGI but maybe it will give us superhuman machines only in well defined contexts. We'll have to squeeze our instructions in a prompt too small for some tasks and get a bot behaving like the main character of the Memento movie (he remembered only the last few minutes and very old memories.)
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• marshmallowmad 3y ago
  I don’t quite understand why context length needs to keep growing. It seems to me like many tasks (e.g. customizing your LLM on your own data) would benefit from the model doing some sort of sped up fine-tuning on any prompt that gets added. That way we don’t have to find all these hacks to find the most relevant context and repeat the same info in prompts. Curious if anyone has insight here as this has caused me some confusion lately!
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• 1024core 3y ago
  While the race to incorporate longer and longer context (2K PaLM -> 32K now) is interesting, I don't think that'll scale. It'll just add too much noise to the history: how do you establish a causal relationship between what you're holding in your hand, versus the million other things (context) that you've seen in the past. You'll end up with spurious correlations. What I think (and this is just me talking out of my ass) will be required is some form of associative long-term memory. Basically, give the model a way to store some embeddings in some form of memory, and then retrieve them based on context: so it doesn't matter if you encountered that item 2 tokens ago, or 2B. At least this is what my current intuition tells me.
  • lucidrains 3y ago
    that line of research is still going. https://github.com/lucidrains/block-recurrent-transformer-pytorch https://github.com/lucidrains/block-recurrent-transformer-py... i think it is worth continuing research on both fronts.
    • 1024core 3y ago
      Of course, I'm not saying "don't do research". I'm just saying that I don't think this context-length war will lead us to long-term sustainable gains.
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  • nathias 3y ago
    this and evaluation based on context that lets it mutate past content and we are set
  • skybrian 3y ago
    On the other hand, it seems like training on large amounts of text for next-token prediction would tend to reduce reliance on spurious correlations? I don’t think this intuitive sort of speculation can predict what it will do.
• logophobia 3y ago
  I've applied the S4 operator to successfully do long-length video classification. It's massively more efficient than a similarly scaled transformer, but it doesn't train as well. Still, even with S4 I got some impressive results, looking forward to more.
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• imustachyou 3y ago
  S4 and its class of state-space models are an impressive mathematical and signal-processing innovation, and I thought it was awesome how they destroyed previous baselines for long-range tasks. Have there been any state-space models adapted for arbitrary text generation? Language models like ChatGPT are trained to predict new words based on the previous ones and are excellent for generation, a harder task than translation or classification. I'm doubtful about the adaptability of text models that deal with fixed-sized input/outputs and don't have an architecture that is as natural for generating indefinitely long sequences.
  • sdenton4 3y ago
    Go read about S4, from these authors. It's about having a learnable state-space model which can be efficiently implemented as either an RNN or (very long) convolution, according to the needs of train or inference.
    • Buttons840 3y ago
      Do these scale as well as transformers? My understanding is that classic RNNs don't scale well, and that is one reason why transformers became popular. As a pleb who doesn't even own a data center, I've been hoping that a superior machine learning architecture will be discovered that doesn't scale well. We would be fortunate if our personal computers end up being half as good as Microsoft's or Amazon's best models; fortunate if the best architecture gains little from an additional 10,000 GPUs. This would help spread the benefits of AI evenly among anyone with a phone or computer -- a utopia compared to the other possibility, that everyone can learn how to build AI, but only those with a few hundred million to throw at a data center can actually control the means of production -- err, I mean, the means of intelligence. Philosophically, this wouldn't be unlike people. Humans are still the greatest intelligence we're aware of, and humans don't scale. I'm hoping computer intelligence ends up not scaling well either.
      • sdenton4 3y ago
        That's the point of having multiple realizations of the same underlying model. The (depthwise) convolutional realization is extremely efficient for training, and the RNN is extremely efficient for inference. The scaling in both of these cases is much better than attention layers - as they discuss in the article.
• Buttons840 3y ago
  If I want to do sequence modelling, let's say, predict the 9th element of the following sequence: [1, 2, 3, 4, 5, 6, 7, 8, 9] -- that is, I know the 8 most recent tokens as my "context", and I want to predict the next, 9 in this case -- Can someone explain to me why a transformer or RNN is better at this than a simple linear layer with an equivalent number of parameters? A linear layer can receive the context [1, 2, 3, 4, 5, 6, 7, 8], properly one-hot encoded / embedded, etc, and predict the next sequence. Can a linear layer do just as well as a transformer? This setup allows linear layers to predict sequences with an arbitrary context size, so why so much hype about transformers and RNNs and other sequence focused architectures? Perhaps the difference is that given the same number of parameters, the transformer uses those parameters to perform easy computations whereas the linear layer just does one gigantic matrix multiplication which isn't very efficient?
  • QuadmasterXLII 3y ago
    The big difference is that the transformer is (approximately) permutation equivariant, which makes a massive difference in generalization and training speed.
    • Buttons840 3y ago
      I see, so [1, 2, 3, 4, 5, 6, 7, 8] is more similar to [5, 3, 1, 7, 8, 2, 4, 6] than with a linear layer? That's what you mean by permutation equivariant? I understand each context input is embedded with its position, but I suppose the transformer can learn to ignore the position and just look at the context as an unordered set?
    • eachro 3y ago
      What makes it approximately permutation equivariant (vs entirely)? As I understand things, if the order is jumbled, the attention matrix does get its rows and cols permuted in the way you'd expect so I'd have thought they'd be entirely permutation equivariant.
      • YetAnotherNick 3y ago
• carra 3y ago
  I'm wondering if, for very long contexts, the most efficient way could be to read and remember the same way we humans do: first do a full read of the first N pages, then summarize that and continue reading. You lose precision of what you read many pages ago, but it allows you to retain much more context by essentially "compressing" the information.
• vagab0nd 3y ago
  This is complete off-topic, but the layout of the website somehow made me think @sama wrote the paper.